Note: I’ve expanded this into a comprehensive guide to Python and Parquet in this post
Parquet Format Partitions
In order to use filters you need to store your data in Parquet format using partitions. Loading a few Parquet columns and partitions out of many can result in massive improvements in I/O performance with Parquet versus CSV. Parquet can partition files based on values of one or more fields and it creates a directory tree for the unique combinations of the nested values, or just one set of directories for one partition column. The PySpark Parquet documentation explains how Parquet works fairly well.
A partition on gender and country would look like this:
path
└── to
└── table
├── gender=male
│ ├── ...
│ │
│ ├── country=US
│ │ └── data.parquet
│ ├── country=CN
│ │ └── data.parquet
│ └── ...
There is also row group partitioning if you need to further partition your data, but most tools only support specifying row group size and you have to do the key-->row group lookup yourself, which is ugly (happy to answer about that in another question).
Writing Partitions with Pandas
You need to partition your data using Parquet and then you can load it using filters. You can write the data in partitions using PyArrow, pandas or Dask or PySpark for large datasets.
For example, to write partitions in pandas:
df.to_parquet(
path='analytics.xxx',
engine='pyarrow',
compression='snappy',
columns=['col1', 'col5'],
partition_cols=['event_name', 'event_category']
)
This lays the files out like:
analytics.xxx/event_name=SomeEvent/event_category=SomeCategory/part-0001.c000.snappy.parquet
analytics.xxx/event_name=SomeEvent/event_category=OtherCategory/part-0001.c000.snappy.parquet
analytics.xxx/event_name=OtherEvent/event_category=SomeCategory/part-0001.c000.snappy.parquet
analytics.xxx/event_name=OtherEvent/event_category=OtherCategory/part-0001.c000.snappy.parquet
Loading Parquet Partitions in PyArrow
To grab events by one property using the partition columns, you put a tuple filter in a list:
import pyarrow.parquet as pq
import s3fs
fs = s3fs.S3FileSystem()
dataset = pq.ParquetDataset(
's3://analytics.xxx',
filesystem=fs,
validate_schema=False,
filters=[('event_name', '=', 'SomeEvent')]
)
df = dataset.to_table(
columns=['col1', 'col5']
).to_pandas()
Filtering with Logical ANDs
To grab an event with two or more properties using AND you just create a list of filter tuples:
import pyarrow.parquet as pq
import s3fs
fs = s3fs.S3FileSystem()
dataset = pq.ParquetDataset(
's3://analytics.xxx',
filesystem=fs,
validate_schema=False,
filters=[
('event_name', '=', 'SomeEvent'),
('event_category', '=', 'SomeCategory')
]
)
df = dataset.to_table(
columns=['col1', 'col5']
).to_pandas()
Filtering with Logical ORs
To grab two events using OR you need to nest the filter tuples in their own lists:
import pyarrow.parquet as pq
import s3fs
fs = s3fs.S3FileSystem()
dataset = pq.ParquetDataset(
's3://analytics.xxx',
filesystem=fs,
validate_schema=False,
filters=[
[('event_name', '=', 'SomeEvent')],
[('event_name', '=', 'OtherEvent')]
]
)
df = dataset.to_table(
columns=['col1', 'col5']
).to_pandas()
Loading Parquet Partitions with AWS Data Wrangler
As another answer mentioned, the easiest way to load data filtering to just certain columns in certain partitions wherever the data is located (locally or in the cloud) is to use the awswrangler module. If you're using S3, check out the documentation for awswrangler.s3.read_parquet() and awswrangler.s3.to_parquet(). The filtering works the same as with the examples above.
import awswrangler as wr
df = wr.s3.read_parquet(
path="analytics.xxx",
columns=["event_name"],
filters=[('event_name', '=', 'SomeEvent')]
)
Loading Parquet Partitions with pyarrow.parquet.read_table()
If you're using PyArrow, you can also use pyarrow.parquet.read_table():
import pyarrow.parquet as pq
fp = pq.read_table(
source='analytics.xxx',
use_threads=True,
columns=['some_event', 'some_category'],
filters=[('event_name', '=', 'SomeEvent')]
)
df = fp.to_pandas()
Loading Parquet Partitions with PySpark
Finally, in PySpark you can use pyspark.sql.DataFrameReader.read_parquet()
import pyspark.sql.functions as F
from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local[1]") \
.appName('Stack Overflow Example Parquet Column Load') \
.getOrCreate()
# I automagically employ Parquet structure to load the selected columns and partitions
df = spark.read.parquet('s3://analytics.xxx') \
.select('event_name', 'event_category') \
.filter(F.col('event_name') == 'SomeEvent')
Hopefully this helps you work with Parquet :)
