Chapter 6: Practical Use Cases: Time-Series Data Compression

Introduction: Mastering Time-Series Compression with OpenZL

Welcome back, future data compression wizard! In our previous chapters, we laid the groundwork for understanding OpenZL’s core concepts – its graph-based approach, the role of codecs, and the power of SDDL. Now, it’s time to put that knowledge into action by tackling one of the most prevalent and critical data types in modern applications: time-series data.

Time-series data, from sensor readings in IoT devices to financial market data and application performance metrics, is ubiquitous. Its sheer volume often poses significant challenges for storage, transmission, and analysis. This is where OpenZL truly shines. Because time-series data inherently possesses a strong, predictable structure (timestamps, values, often ordered), it’s a perfect candidate for OpenZL’s “format-aware” compression.

In this chapter, we’ll dive deep into practical applications. You’ll learn how to define the structure of time-series data using OpenZL’s Simple Data Description Language (SDDL), construct effective compression plans by chaining specialized codecs, and implement these plans in Python to achieve impressive compression ratios. Get ready to transform bulky datasets into lean, efficient streams!

Core Concepts: OpenZL for Time-Series Data

Before we start coding, let’s solidify our understanding of why OpenZL is such a powerful tool for time-series data.

What is Time-Series Data?

At its heart, time-series data is a sequence of data points indexed by time. Think of it as a list of observations, each associated with a specific timestamp.

For example:

Sensor Readings: Temperature (timestamp, value)
Stock Prices: (timestamp, open, high, low, close)
Server Metrics: (timestamp, CPU_usage, Memory_usage)

What makes this data unique are two key characteristics:

Temporal Order: The order of data points matters and is defined by time.
Structural Repetition: Values often change incrementally, or follow trends and seasonal patterns. Timestamps themselves are also highly structured (e.g., increasing monotonically).

Why OpenZL Excels with Time-Series

Traditional “black-box” compressors (like Gzip or Zstd applied directly to raw text) treat data as an unstructured stream of bytes. They look for general patterns but don’t understand the meaning or structure of the data.

OpenZL, on the other hand, is format-aware. This means:

Schema-Driven Compression: By defining the data’s structure using SDDL, OpenZL knows exactly what it’s compressing (e.g., “this is a 64-bit timestamp, this is a 32-bit float”). This allows it to apply highly specialized codecs to specific parts of your data.
Codec Chaining: Time-series data often benefits from multiple stages of compression. For instance, timestamps might be best compressed using delta encoding, while floating-point values might benefit from specialized algorithms like Gorilla encoding before a final general-purpose compression step. OpenZL’s graph model allows you to easily chain these codecs together.
Optimization: With SDDL, OpenZL can even learn the best compression plan for your specific data, optimizing for speed or ratio.

Let’s visualize a common compression pipeline for time-series data using OpenZL’s graph model:

flowchart TD A[Raw Time-Series Data] -->|1. Described by SDDL| B[OpenZL Engine] B -->|2. Apply Delta Encoding| C{Delta Codec} C -->|3. Apply Specialized Value Codec| D{Gorilla/Floating-Point Codec} D -->|4. Apply General Compressor| E{Zstd/Snappy Codec} E -->|5. Compressed Output| F[Storage/Transmission]

In this diagram:

A: Our original time-series data, perhaps a list of sensor readings.
B: The OpenZL engine, which understands the data’s structure thanks to SDDL.
C: A Delta Codec compresses the differences between consecutive timestamps and values, which are typically small for time-series.
D: A specialized codec, like Gorilla, is excellent for compressing floating-point values that change slowly over time.
E: A general-purpose compressor like Zstd or Snappy then takes the output of the specialized codecs and applies a final, highly efficient compression pass.
F: The compact, compressed data, ready for storage or network transfer.

The Role of SDDL

SDDL, or Simple Data Description Language, is your blueprint for telling OpenZL about your data. For time-series, this means defining the fields (e.g., timestamp, value, sensor_id), their types (e.g., u64 for unsigned 64-bit integer, f32 for 32-bit float), and their order.

Understanding your data’s structure with SDDL is the first, crucial step to unlocking OpenZL’s full potential. Without it, OpenZL can’t intelligently apply its specialized codecs.

Step-by-Step Implementation: Compressing a Simple Time-Series

Let’s get our hands dirty and implement a basic time-series compression pipeline using OpenZL. We’ll simulate some sensor data.

Prerequisites: Before you start, ensure you have Python installed (version 3.8+ is recommended). As of January 2026, OpenZL is actively developed by Meta. You can typically install it via pip. Note: The specific openzl package name and its exact API might evolve rapidly. Always refer to the official OpenZL GitHub repository for the most up-to-date installation instructions and API documentation.

pip install openzl

(If the above command fails or yields an error, please consult the official OpenZL GitHub repository at https://github.com/facebook/openzl for the latest installation instructions.)

Step 1: Define the SDDL Schema for Our Time-Series Data

We’ll start by defining a simple structure for our sensor readings: a timestamp and a temperature value.

Create a new Python file, say compress_timeseries.py.

# compress_timeseries.py

# Step 1: Define the SDDL Schema
# SDDL (Simple Data Description Language) describes the structure of our data.
# Here, we define a 'SensorReading' struct with a 64-bit timestamp and a 32-bit float temperature.
# The 'stream' keyword indicates that we expect a sequence of these readings.
timeseries_sddl_schema = """
struct SensorReading {
    timestamp: u64,
    temperature: f32,
}
stream SensorReading;
"""

print("SDDL Schema Defined.")

Explanation:

struct SensorReading { ... }: We define a structure named SensorReading.
timestamp: u64: This field holds the timestamp, an unsigned 64-bit integer.
temperature: f32: This field holds the temperature, a 32-bit floating-point number.
stream SensorReading;: This tells OpenZL that our input data will be a continuous stream of SensorReading objects, not just a single one. This is crucial for time-series data.

Step 2: Generate Sample Time-Series Data

Next, let’s create some synthetic sensor data that our schema can describe. We’ll simulate temperature readings that gradually increase.

Add the following code to compress_timeseries.py:

import time
import random
# ... (previous SDDL schema definition) ...

# Step 2: Generate Sample Time-Series Data
def generate_sample_data(num_points=100, start_temp=20.0):
    data = []
    current_time = int(time.time() * 1000) # Milliseconds since epoch
    current_temperature = start_temp

    for i in range(num_points):
        # Simulate a timestamp increment
        current_time += random.randint(1000, 5000) # 1-5 seconds apart
        # Simulate temperature fluctuation
        current_temperature += random.uniform(-0.1, 0.2)
        # Ensure temperature stays reasonable
        current_temperature = max(15.0, min(30.0, current_temperature))

        data.append({
            "timestamp": current_time,
            "temperature": round(current_temperature, 2) # Round for cleaner data
        })
    return data

sample_data = generate_sample_data(num_points=10) # Let's start with 10 points for clarity
print(f"\nGenerated {len(sample_data)} sample data points:")
for i, point in enumerate(sample_data):
    if i < 3 or i > len(sample_data) - 4: # Show first/last few
        print(f"  {point}")
    elif i == 3:
        print("  ...")

Explanation:

generate_sample_data: This function creates a list of dictionaries, where each dictionary represents a SensorReading matching our SDDL schema.
current_time and current_temperature: We simulate gradual changes, which is typical for time-series data and helps showcase the effectiveness of delta encoding.
num_points=10: We start with a small number of points to keep the output manageable. Feel free to increase this later!

Step 3: Create an OpenZL Compression Plan

Now, we’ll tell OpenZL how to compress our SensorReading stream. This involves defining a “plan” that specifies which codecs to apply to which parts of the data.

Add the following to compress_timeseries.py:

from openzl import OpenZL, CodecGraph, Codec
# ... (previous code) ...

# Step 3: Create an OpenZL Compression Plan
# We need to define a CodecGraph that specifies the compression pipeline.
# For time-series, delta encoding is often the first step for timestamps and values.
# Then, a general-purpose compressor like Snappy or Zstd provides a final pass.

# For illustration, let's assume OpenZL provides codecs like Delta and Snappy.
# In a real scenario, you'd import specific codec classes provided by the OpenZL library.

# Placeholder for actual codec classes (these names are illustrative)
class MockDeltaCodec(Codec):
    def compress(self, data): return b"compressed_delta_" + data
    def decompress(self, data): return data.replace(b"compressed_delta_", b"")
    def __repr__(self): return "DeltaCodec"

class MockSnappyCodec(Codec):
    def compress(self, data): return b"compressed_snappy_" + data
    def decompress(self, data): return data.replace(b"compressed_snappy_", b"")
    def __repr__(self): return "SnappyCodec"

# In a real OpenZL library, you would instantiate actual codecs:
# from openzl.codecs import DeltaCodec, SnappyCodec
# delta_codec = DeltaCodec()
# snappy_codec = SnappyCodec()

# For this example, we'll use our mock codecs.
delta_codec = MockDeltaCodec()
snappy_codec = MockSnappyCodec()

# Build the codec graph for our SensorReading stream
# We're telling OpenZL to apply delta encoding to the timestamp and temperature fields
# of each SensorReading, then apply Snappy compression to the entire resulting stream.
compression_plan = CodecGraph(
    schema=timeseries_sddl_schema,
    # This is a simplified representation. Actual OpenZL API might differ.
    # Conceptually, we're mapping parts of our SDDL schema to codecs.
    # For a 'stream SensorReading', we'd typically define a pipeline for the stream itself.
    # Let's assume a simplified API for chaining codecs for the whole stream.
    # A more advanced API would allow field-specific codecs.
    # For now, we chain them for the entire serialized stream.
    codecs=[delta_codec, snappy_codec] # Apply sequentially
)

# Initialize OpenZL with our plan
openzl_instance = OpenZL(compression_plan)

print("\nOpenZL Compression Plan Created.")
print(f"Plan codecs: {openzl_instance.codec_graph.codecs}")

Explanation:

from openzl import OpenZL, CodecGraph, Codec: We import the necessary components from the OpenZL library.
Mock Codecs: Since the exact Python API for OpenZL’s codecs is still emerging as of early 2026, we use MockDeltaCodec and MockSnappyCodec to simulate their behavior. In a real OpenZL setup, you would import and use actual codec classes (e.g., from openzl.codecs import DeltaCodec, SnappyCodec).
compression_plan = CodecGraph(...): This is where we build our compression pipeline.
- schema=timeseries_sddl_schema: We link our SDDL schema to the plan.
- codecs=[delta_codec, snappy_codec]: This defines the sequence of codecs. Conceptually, the raw data will first go through DeltaCodec, and its output will then be fed into SnappyCodec. For a stream type, OpenZL intelligently applies these to the elements within the stream based on the schema.

Step 4: Compress the Data

With our data and plan ready, let’s compress!

Add the following to compress_timeseries.py:

import json
# ... (previous code) ...

# Step 4: Compress the Data
# OpenZL expects data that conforms to the SDDL schema.
# For Python, this typically means a list of dictionaries that match the struct.

# First, we need to serialize our Python objects into a format OpenZL can process.
# OpenZL's API would handle this internally based on the SDDL.
# For our mock example, let's simulate the data being ready for compression.
# In a real OpenZL setup, you'd pass the `sample_data` list directly to `openzl_instance.compress()`.

# Let's simulate the internal serialization that OpenZL would do.
# For a stream of SensorReading, it might concatenate the binary representations.
serialized_data_for_mock = b""
for point in sample_data:
    # This is a simplified representation. Actual serialization would convert
    # u64 and f32 into their binary forms (e.g., struct.pack).
    serialized_data_for_mock += f"{point['timestamp']},{point['temperature']}|".encode('utf-8')

compressed_data = openzl_instance.compress(serialized_data_for_mock)

print(f"\nOriginal (simulated serialized) data size: {len(serialized_data_for_mock)} bytes")
print(f"Compressed data size: {len(compressed_data)} bytes")
print(f"Compressed data (first 50 bytes): {compressed_data[:50]}...")

Explanation:

openzl_instance.compress(sample_data): This is the core function call. OpenZL takes our Python list of dictionaries, internally serializes it according to the timeseries_sddl_schema, and then applies the compression_plan’s codecs in sequence.
Serialization Note: The comment about serialized_data_for_mock highlights that OpenZL itself handles the conversion of your structured Python data into a raw byte stream that the codecs can operate on. For our mock codecs, we’re just simulating this intermediate step. The actual openzl.compress() method would likely take your list[dict] directly.
We print the sizes to get an idea of the compression ratio. With mock codecs, the “compression” is just adding prefixes, but with real codecs, you’d see a significant reduction.

Step 5: Decompress and Verify

To ensure our compression worked correctly, we need to decompress the data and verify it matches the original.

Add the following to compress_timeseries.py:

# ... (previous code) ...

# Step 5: Decompress and Verify
decompressed_serialized_data = openzl_instance.decompress(compressed_data)

# Again, OpenZL's API would handle deserialization back to Python objects.
# For our mock, we reverse the simulated serialization.
decompressed_data = []
for item_str in decompressed_serialized_data.decode('utf-8').strip('|').split('|'):
    if item_str:
        parts = item_str.split(',')
        if len(parts) == 2:
            try:
                decompressed_data.append({
                    "timestamp": int(parts[0]),
                    "temperature": float(parts[1])
                })
            except ValueError:
                print(f"Warning: Could not parse '{item_str}' during mock deserialization.")
                continue

print(f"\nDecompressed {len(decompressed_data)} data points.")
print(f"Decompressed data (first 3 points): {decompressed_data[:3]}")

# Verification: Compare original and decompressed data
# Note: Floating point comparisons need care due to precision.
# For simplicity, we'll compare rounded values.
is_match = True
if len(sample_data) != len(decompressed_data):
    is_match = False
else:
    for original, decompressed in zip(sample_data, decompressed_data):
        if original["timestamp"] != decompressed["timestamp"] or \
           abs(original["temperature"] - decompressed["temperature"]) > 0.01: # Allow small float diff
            is_match = False
            break

if is_match:
    print("\nVerification successful: Original and decompressed data match!")
else:
    print("\nVerification failed: Original and decompressed data DO NOT match.")
    print("Original (last point):", sample_data[-1])
    print("Decompressed (last point):", decompressed_data[-1])

Explanation:

openzl_instance.decompress(compressed_data): This reverses the compression process, applying the codecs in the reverse order.
Deserialization Note: Similar to serialization, OpenZL would handle converting the raw decompressed bytes back into structured Python objects. We’re simulating this step for our mock codecs.
Verification: It’s critical to verify that the decompressed data is identical to the original. This confirms the integrity of the compression and decompression process. We use a small tolerance for floating-point comparisons.

Run your compress_timeseries.py file:

python compress_timeseries.py

You should see output indicating the schema definition, data generation, plan creation, simulated compression, and successful verification! While our mock codecs don’t show true compression, they illustrate the OpenZL workflow.

Mini-Challenge: Extend the Sensor Data

Now it’s your turn to get creative!

Challenge: Modify the SensorReading SDDL schema to include an additional field: humidity: f32. Then, update the generate_sample_data function to include this new humidity value in each data point. Finally, run your script again and observe how OpenZL (conceptually) handles the new schema.

Hint:

For the SDDL, simply add humidity: f32, inside the SensorReading struct.
For generate_sample_data, add a humidity key-value pair to each dictionary in the data list. You can simulate humidity changing similarly to temperature.
Remember to adjust the mock serialization/deserialization if you want the mock codecs to “process” the new field, but the OpenZL framework itself would handle it automatically with real codecs.

What to Observe/Learn: This exercise reinforces your understanding of:

How SDDL directly dictates the structure OpenZL expects.
The ease with which you can adapt your data schema and OpenZL will adjust its internal processing (assuming appropriate codecs are in the plan).
The importance of consistency between your data generation and your SDDL definition.

Common Pitfalls & Troubleshooting

Even with a powerful framework like OpenZL, you might encounter issues. Here are a few common pitfalls when working with time-series data:

SDDL Schema Mismatch:
- Pitfall: Your Python data (e.g., field names, data types) doesn’t exactly match your timeseries_sddl_schema. For instance, if your SDDL specifies temperature: f32 but your Python data has temp_val: float.
- Troubleshooting: OpenZL will likely throw a serialization error, indicating it cannot map your input data to the defined schema. Carefully compare your SDDL struct definition with the keys and types in your Python dictionaries. Ensure u64 for timestamps and f32 for floats are correctly represented in Python (e.g., int for u64, float for f32).
Ineffective Codec Chaining:
- Pitfall: You’ve chosen codecs that aren’t well-suited for time-series data, or you’ve put them in an inefficient order. For example, using a generic compressor before a delta encoder on timestamps might yield suboptimal results.
- Troubleshooting: OpenZL’s strength is its modularity. Experiment with different codec combinations. For time-series, always consider DeltaCodec for monotonic values (like timestamps or slowly changing sensor readings) and specialized codecs like GorillaCodec for floating-point data, followed by a general-purpose compressor like ZstdCodec or SnappyCodec. OpenZL also offers features to train a plan for optimal performance, which you’d explore in more advanced usage.
Large Data Volume and Performance:
- Pitfall: While OpenZL is efficient, compressing very large streams of data can still be CPU or memory intensive, especially with complex codec graphs.
- Troubleshooting:
  - Batching: Process data in smaller chunks rather than trying to compress an entire multi-gigabyte file at once.
  - Profiling: Use Python’s profiling tools (cProfile) to identify bottlenecks in your data generation, serialization, or compression steps.
  - Codec Selection: Faster codecs (like Snappy) might offer slightly lower compression ratios but significantly better throughput than slower, higher-ratio codecs (like Zstd Ultra). Choose based on your performance requirements.

Summary

Congratulations! You’ve successfully navigated the practical application of OpenZL for time-series data. In this chapter, we covered:

Understanding Time-Series: Why this data type is perfect for OpenZL due to its structured and temporal nature.
SDDL for Schemas: How to precisely define the structure of your time-series data using OpenZL’s Simple Data Description Language.
Codec Graph Construction: Building an intelligent compression pipeline by chaining specialized codecs like delta encoding and general-purpose compressors.
Hands-on Implementation: A step-by-step guide to generating sample data, creating an OpenZL plan, compressing, and verifying your time-series data in Python.
Mini-Challenge: Extending your schema and data to reinforce your understanding.
Troubleshooting: Practical advice for common issues like schema mismatches and codec selection.

You now have a solid foundation for compressing structured time-series data efficiently using OpenZL. This skill is invaluable for managing data from IoT, monitoring systems, and other high-volume applications.

In the next chapter, we’ll explore another compelling use case: compressing semi-structured data like JSON or log files, and how OpenZL adapts to data that isn’t as rigidly defined as time-series.

References

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.

Chapter 6: Practical Use Cases: Time-Series Data Compression

Table of Contents

Introduction: Mastering Time-Series Compression with OpenZL

Core Concepts: OpenZL for Time-Series Data

What is Time-Series Data?

Why OpenZL Excels with Time-Series

The Role of SDDL

Step-by-Step Implementation: Compressing a Simple Time-Series

Step 1: Define the SDDL Schema for Our Time-Series Data

Step 2: Generate Sample Time-Series Data

Step 3: Create an OpenZL Compression Plan

Step 4: Compress the Data

Step 5: Decompress and Verify

Mini-Challenge: Extend the Sensor Data

Common Pitfalls & Troubleshooting

Summary

References