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Dagster Data Engineering Glossary:


Checkpointing

Saving the state of a process at certain points so that it can be restarted from that point in case of failure.

Definition of checkpointing:

Checkpointing, in the context of data processing and pipeline execution, refers to the practice of saving the state of a process at certain points so that it can be restarted from that point in case of failure, rather than from the beginning. This can be crucial for long-running processes and pipelines, ensuring that progress is not lost and that computation resources are not wasted.

Key Aspects of Checkpointing

Fault Tolerance: In distributed systems, failures are inevitable. Checkpointing allows a system to recover from failures by restarting the processing from the last saved state rather than reprocessing all the data from scratch. This minimizes data loss and processing time in case of hardware failures, software bugs, or network issues.

State Management: The "state" in data processing refers to the intermediate data and computations that a task depends on. Checkpointing involves saving this state periodically so that it can be reloaded and used to resume processing.

Performance Optimization: By reducing the amount of reprocessing needed after a failure, checkpointing helps optimize resource usage and overall performance of data pipelines. This is particularly important in large-scale data processing where reprocessing from the start can be very time-consuming and resource-intensive.

Consistency: Checkpointing ensures data consistency by maintaining a coherent state of data processing. If a failure occurs, the system can roll back to the last consistent checkpoint and continue processing, avoiding partial and inconsistent states.

Checkpointing Mechanisms

Checkpointing can be implemented in various ways, depending on the system and the specific requirements:

Event-Based Checkpointing: Checkpoints are taken based on certain events, such as the completion of a specific data batch or the processing of a certain number of records. This is typically more efficient than time-based checkpointing and more reliable than manual approaches.

Time-Based or Scheduled Checkpointing: The system takes checkpoints at regular time intervals. This is simple to implement but might not always be optimal in terms of resource usage. This is typically an older approach used in slow-moving legacy systems.

Manual Checkpointing: Here, developers have control over when checkpoints are created, allowing for more fine-grained control over the process. This is most suited for one-off system changes and upgrades.

Implementation Considerations

When implementing checkpointing, several factors need to be considered:

Storage: The choice of storage for checkpoints is crucial. It needs to be reliable and durable, often involving distributed storage systems like AWS S3, or similar. Frequency: Deciding the frequency of checkpoints involves a trade-off between fault tolerance and performance. Frequent checkpoints provide better fault tolerance but may incur higher overhead. State Size: The size of the state being saved can impact the efficiency of checkpointing. Efficient serialization and compression techniques may be needed to manage large state sizes.

Checkpointing in Dagster

Dagster implicitly supports a form of checkpointing through asset materializations. This allows for more efficient pipeline execution, as computations can be resumed based on the latest successful asset materializations rather than starting from scratch.

Dagster assets represent units of data that are the outputs of computations. When you define assets in Dagster, you create a graph of these assets, specifying how they depend on one another. Each time an asset is computed or updated ("materialized"), Dagster records this event, effectively creating a checkpoint in the asset's lifecycle. This metadata can serve a similar purpose to checkpoints, as it allows users to understand the state of their data assets at any given time and to restart computations from the last known good state if necessary.

Checkpointing is a technique used in data engineering to save the state of a process at specific intervals. This allows for recovery from failures without having to restart the entire process. Here's an example of checkpointing in a data processing pipeline using Python. We'll simulate a data processing task that reads data in chunks, processes it, and uses checkpointing to save the progress.

A Python example of Checkpointing

Let's demonstrate checkpointing in Python using a CSV file to simulate data chunks and a checkpoint file to store the progress. First, let's create a sample CSV file with dummy data:

import csv

# Sample data
data = [
    ['id', 'name', 'value'],
    [1, 'Alice', 10],
    [2, 'Bob', 20],
    [3, 'Charlie', 30],
    [4, 'David', 40],
    [5, 'Eve', 50],
    [6, 'Jacob', 60],
    [7, 'Porshe', 70],
    [8, 'Le', 80],
    [9, 'Antoine', 90],
    [10, 'Katarina', 100]
]

# Write sample data to CSV
with open('data.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)

Checkpointing Example

Now, let's write a Python script to process this data in chunks and use checkpointing to save the progress. We will build in a delay to give us the chance to interact with the program.

import csv
import os
import time

# Function to read the last checkpoint
def read_checkpoint():
    if os.path.exists('checkpoint.txt'):
        with open('checkpoint.txt', 'r') as file:
            return int(file.read().strip())
    return 0

# Function to write the current checkpoint
def write_checkpoint(checkpoint):
    with open('checkpoint.txt', 'w') as file:
        file.write(str(checkpoint))

# Function to process data
def process_data(row):
    # Simulate data processing
    print(f"Processing row: {row}")

# Main function to read and process data with checkpointing
def main():
    checkpoint = read_checkpoint()
    print(f"Starting from checkpoint: {checkpoint}")

    with open('data.csv', 'r') as file:
        reader = csv.reader(file)
        header = next(reader)  # Skip header

        current_position = 0
        for row in reader:
            if current_position >= checkpoint:
                process_data(row)
                checkpoint = current_position + 1
                write_checkpoint(checkpoint)

                # Delay for 5 seconds to allow interruption
                time.sleep(5)
            current_position += 1

if __name__ == "__main__":
    main()

To break down the steps:

Checkpoint Read/Write Functions: read_checkpoint(): Reads the last checkpoint from the file, and then write_checkpoint(checkpoint) writes the current checkpoint to the file.

Data Processing: process_data(row): Simulates processing a data row.

Main Function: Here we read the last checkpoint, opens the CSV file and skips the header, process data rows starting from the last checkpoint, then update the checkpoint after processing each row.

Let's run the Script

If you now execute the two scripts above in sequence, the second one will start from the beginning and process all rows form the test file. You can stop the script mid-execution (e.g., using a keyboard interrupt). When you run the script again, it will resume processing from the last saved checkpoint.

This example demonstrates a simple yet effective way to implement checkpointing in a data processing pipeline, allowing for resumption of work after interruptions without reprocessing already completed data.

Your output may look something like this:

% python checkpoint.py
Starting from checkpoint: 0
Processing row: ['1', 'Alice', '10']
Processing row: ['2', 'Bob', '20']
^C KeyboardInterrupt

% python checkpoint.py
Starting from checkpoint: 2
Processing row: ['3', 'Charlie', '30']
Processing row: ['4', 'David', '40']
Processing row: ['5', 'Eve', '50']
^C KeyboardInterrupt

% python checkpoint.py
Starting from checkpoint: 5
Processing row: ['6', 'Jacob', '60']
^C KeyboardInterrupt

% python checkpoint.py
Starting from checkpoint: 6
Processing row: ['7', 'Porshe', '70']
Processing row: ['8', 'Le', '80']
Processing row: ['9', 'Antoine', '90']
Processing row: ['10', 'Katarina', '100']

Other data engineering terms related to
Data Management:
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Append

Adding or attaching new records or data items to the end of an existing dataset, database table, file, or list.
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Archive

Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. Store data for long-term retention and compliance.
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Augment

Add new data or information to an existing dataset to enhance its value.
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Auto-materialize

The automatic execution of computations and the persistence of their results.
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Backup

Create a copy of data to protect against loss or corruption.
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Batch Processing

Process large volumes of data all at once in a single operation or batch.
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Cache

Store expensive computation results so they can be reused, not recomputed.
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Categorize

Organizing and classifying data into different categories, groups, or segments.
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Deduplicate

Identify and remove duplicate records or entries to improve data quality.
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Deserialize

Deserialization is essentially the reverse process of serialization. See: 'Serialize'.
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Dimensionality

Analyzing the number of features or attributes in the data to improve performance.
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Encapsulate

The bundling of data with the methods that operate on that data.
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Enrich

Enhance data with additional information from external sources.
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Export

Extract data from a system for use in another system or application.
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Graph Theory

A powerful tool to model and understand intricate relationships within our data systems.
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Idempotent

An operation that produces the same result each time it is performed.
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Index

Create an optimized data structure for fast search and retrieval.
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Integrate

Combine data from different sources to create a unified view for analysis or reporting.
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Lineage

Understand how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.
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Linearizability

Ensure that each individual operation on a distributed system appear to occur instantaneously.
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Materialize

Executing a computation and persisting the results into storage.
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Memoize

Store the results of expensive function calls and reusing them when the same inputs occur again.
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Merge

Combine data from multiple datasets into a single dataset.
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Model

Create a conceptual representation of data objects.
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Monitor

Track data processing metrics and system health to ensure high availability and performance.
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Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.
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Parse

Interpret and convert data from one format to another.
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Partition

Data partitioning is a technique that data engineers and ML engineers use to divide data into smaller subsets for improved performance.
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Prep

Transform your data so it is fit-for-purpose.
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Preprocess

Transform raw data before data analysis or machine learning modeling.
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Primary Key

A unique identifier for a record in a database table that helps maintain data integrity.
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Replicate

Create a copy of data for redundancy or distributed processing.

Scaling

Increasing the capacity or performance of a system to handle more data or traffic.
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Schema Inference

Automatically identify the structure of a dataset.
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Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.
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Secondary Index

Improve the efficiency of data retrieval in a database or storage system.
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Software-defined Asset

A declarative design pattern that represents a data asset through code.
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Synchronize

Ensure that data in different systems or databases are in sync and up-to-date.
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Validate

Check data for completeness, accuracy, and consistency.
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Version

Maintain a history of changes to data for auditing and tracking purposes.
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