Data Partitioning

Data partitioning definition:

Partitioning is a technique that helps data engineers and ML engineers organize data and the computations that produce that data. Partitioning also makes data pipelines more performant and cost-efficient by letting them operate on subsets of data instead of all of it at once.

Below we share some starter examples on partitioning in Python. For more advanced information on partitioning, read Partitions in Data Pipelines.

Partitioning considerations in data engineering:

If you are new to partitioning in data pipelines, here are some key things to consider:

• Choose the appropriate partition key: The partition key should be chosen based on the most common types of queries that will be performed on the data. A good partition key will ensure that the data is distributed evenly across partitions and minimize the need for shuffling data during query processing.
• Avoid over-partitioning: Over-partitioning can negatively impact query performance as it increases the number of small partitions that need to be processed. It can also result in increased storage requirements and complexity. A good rule of thumb is to limit the number of partitions to 10,000 or less (although systems like Dagster can support up to 25,000 partitions per asset, beyond which you will see performance degradation in the UI).
• Use a consistent partitioning scheme: Consistency in the partitioning scheme ensures that the same data is stored in the same partition across different runs of the pipeline. This can help to reduce the complexity of data processing and improve query performance.
• Use partition pruning: Most modern data processing systems support partition pruning, which allows queries to scan only the relevant partitions instead of scanning the entire dataset. This can significantly improve query performance, especially when dealing with large datasets.
• Consider data skew: Data skew can occur when the data is not evenly distributed across partitions, resulting in some partitions processing significantly more data than others. This can lead to longer query times for those partitions and can impact overall query performance. Techniques such as data shuffling or using a different partition key can be used to mitigate data skew.
• Monitor partition usage: It is important to monitor partition usage to identify any hotspots or data skew issues that may impact query performance. Regularly analyzing the partition usage can help to identify opportunities for optimization and fine-tuning the partitioning scheme.
• Plan for future growth: As the data grows, the partitioning scheme may need to be adjusted to ensure optimal query performance. It is important to plan for future growth and be prepared to adjust the partitioning scheme as needed.

A simple example of partitioning data in Python:

Let's consider a very simple example where we want to partition a list of integers into two partitions, say one for even numbers and one for odd numbers. Here's a simple Python script that does that:

def partition_data(data):
    even_partition = []
    odd_partition = []

    for num in data:
        if num % 2 == 0:
            even_partition.append(num)
        else:
            odd_partition.append(num)

    return even_partition, odd_partition

# Now let's use the function with some data
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

even_partition, odd_partition = partition_data(data)

print("Even Partition: ", even_partition)
print("Odd Partition: ", odd_partition)

When you run this script, it would print:

Even Partition:  [2, 4, 6, 8, 10]
Odd Partition:  [1, 3, 5, 7, 9]

So in effect we have partitioned our dataset into two, based on a business rule. The sum of all of your partitions should constitute your entire dataset with no duplicate entries. The most common form of partitioning would be by date (data partitioned by day, week, or month for example), but other partitioning approaches are also used.

Partitioning using Parquet files in Pandas

Let's look at a slightly more advanced example using parquet and dataframes. Pandas needs the pyarrow or fastparquet library to handle Parquet files. You can install it with pip (pip install pyarrow or pip install fastparquet).

Please note that you need to have other Python libraries like Pandas installed in your Python environment to run this code.

Here we will first write a parquet file to disk, then retrieve the same file before we partition it (yes, this is just an example, not a real life practice!)

import pandas as pd

# Sample data
data = {
    'name': ['Thom Yorke', 'Jonny Greenwood', 'Ed OBrien', 'Philip Selway', 'Colin Greenwood'],
    'role': ['vocals', 'guitar', 'guitar', 'drums', 'bass'],
    'created_date': ['1968-10-07', '1971-10-05', '1968-04-15', '1967-05-23', '1969-06-26'],
}

# Create a DataFrame from the data
df = pd.DataFrame(data)

# Write the DataFrame to a Parquet file
df.to_parquet('radiohead.parquet')

# ------------------------------------------

# read data from parquet file
df = pd.read_parquet('radiohead.parquet')

# Convert 'created_date' column to datetime
df['created_date'] = pd.to_datetime(df['created_date'])

# Extract year and month from 'created_date' to a new column 'year_month'
df['year_month'] = df['created_date'].dt.to_period('M')

# Now group the dataframe by 'year_month'
grouped = df.groupby('year_month')

# Now each group in 'grouped' is a partition of customers created in the same month
for name, group in grouped:
    print(f"{name}:")
    print(group)

Running the script will partition the group Radiohead into four partitions based on the birth year of the band members:

1967:
            name   role created_date  year
3  Philip Selway  drums   1967-05-23  1967
1968:
         name    role created_date  year
0  Thom Yorke  vocals   1968-10-07  1968
2   Ed OBrien  guitar   1968-04-15  1968
1969:
              name  role created_date  year
4  Colin Greenwood  bass   1969-06-26  1969
1971:
              name    role created_date  year
1  Jonny Greenwood  guitar   1971-10-05  1971