The Ultimate Guide to Time Series Data With Apache Cassandra - Part 1: Fundamentals

• Wed, Apr 30, 2025
• 6-minute read

Time series data represents one of the most common and challenging workloads for modern data platforms. From IoT device metrics to financial market data, the ability to efficiently store and query chronological measurements at scale has become a critical requirement for many organizations. After years of helping clients implement time series solutions with Apache Cassandra, I’ve developed a set of best practices that can make the difference between a system that struggles and one that scales effortlessly.

In this first part of a comprehensive guide, I’ll break down the fundamental concepts of time series data modeling in Cassandra, and explain why this database is particularly well-suited for these workloads when configured correctly. This guide builds upon principles I’ve discussed in my performance and cost optimization series, where I covered how factors like streaming operations, compaction strategies, and repair processes impact node density and overall cost efficiency.

What Makes Time Series Data Special?

Time series data has several unique characteristics that influence how we should store and access it:

1. Write-heavy pattern: Most time series workloads generate data continuously, making write efficiency critical
2. Sequential insertion: Data typically arrives in timestamp order
3. Immutability: Historical data rarely changes after insertion
4. Query patterns: Reads commonly access recent data and perform time-range scans
5. Aggregation needs: Raw data often needs to be summarized across different time dimensions

Cassandra’s architecture aligns perfectly with these characteristics, but only when we design our data model with these patterns in mind.

The Building Blocks of Time Series Models in Cassandra

Before diving into specific patterns, let’s establish some foundational concepts that will guide our approach:

Time Bucketing: Essential for Scalability

Rather than storing each measurement in its own row, we group measurements into time buckets. This simple technique dramatically improves both write and read performance for several reasons:

-- Without time bucketing (problematic for high-frequency data)
CREATE TABLE sensor_data_raw (
  sensor_id uuid,
  timestamp timestamp,
  value double,
  PRIMARY KEY (sensor_id, timestamp)
);

-- With hourly time bucketing (much more efficient)
CREATE TABLE sensor_data_bucketed (
  sensor_id uuid,
  hour timestamp,
  timestamp timestamp,
  value double,
  PRIMARY KEY ((sensor_id, hour), timestamp)
);

The bucketed approach offers several advantages:

• Controls partition sizes more effectively
• Reduces the number of SSTables Cassandra must merge during compaction
• Enables efficient time-range queries without excessive partition scanning

In my experience, choosing the right bucket size is crucial. For most applications, hourly or daily buckets work well, but this depends on your ingestion rate and query patterns.

TTL Management: The Key to Controlling Storage Growth

Time series data can quickly consume massive amounts of storage. Effective TTL (Time To Live) strategies are essential for managing this growth:

-- Setting a 30-day TTL at table level
CREATE TABLE recent_metrics (
  sensor_id uuid,
  hour timestamp,
  timestamp timestamp,
  value double,
  PRIMARY KEY ((sensor_id, hour), timestamp)
) WITH default_time_to_live = 2592000;  -- 30 days in seconds

While table-level TTLs are convenient, they don’t always provide the flexibility needed for sophisticated retention policies. In my next post, I’ll cover advanced TTL strategies including:

• Multi-table approaches for different retention periods
• Downsampling techniques to preserve summary data while expiring raw measurements
• Automated processes for managing data lifecycle

Partition Key Design for Time Series Data

The choice of partition key significantly impacts Cassandra’s performance with time series data. Here’s what I’ve found works best:

1. Composite partition keys: Combining an entity ID with a time bucket
2. Avoiding hotspots: Ensuring write load is distributed across partitions
3. Sizing for efficiency: Targeting 10-100MB partition sizes

For example, in a system monitoring application tracking servers:

CREATE TABLE server_metrics (
  server_id uuid,
  metric_name text,
  day date,
  timestamp timestamp,
  value double,
  PRIMARY KEY ((server_id, metric_name, day), timestamp)
);

This design ensures that each day’s metrics for each server/metric combination are stored in a separate partition, distributing the write load while keeping related data together.

Real-World Performance Considerations

In production environments, several additional factors become important:

Compression Settings Matter

With time series data, compression can dramatically reduce storage requirements and improve read performance:

ALTER TABLE sensor_data 
WITH compression = {
  'class': 'LZ4Compressor',
  'chunk_length_in_kb': 16
};

I’ve seen compression ratios of 5:1 or better on many time series workloads, especially with numeric data. The smaller chunk size (16KB vs. the default 64KB) often works better for time series queries that access small ranges. I’ll explore compression optimization in greater detail in the upcoming compression performance post in my cost optimization series.

Compaction Strategy Selection

As of Cassandra 5.0, the UnifiedCompactionStrategy (UCS) is now the recommended choice for time series workloads, replacing the older TimeWindowCompactionStrategy (TWCS):

ALTER TABLE sensor_data 
WITH compaction = {
  'class': 'UnifiedCompactionStrategy',
  'scaling_parameters': 'T4'
};

This recommendation comes from extensive testing and real-world deployments. As I discussed in my compaction strategies post, UCS provides superior control over SSTable sizes and significantly improves read performance while maintaining the write-friendliness needed for time series data. The density-based approach used by UCS is particularly effective for time series workloads, which often feature a mix of recent hot data and older cold data.

Node Density Considerations for Time Series

Time series workloads often involve substantial data volumes, making node density a critical factor in cost optimization. When designing your cluster for time series data:

1. Token allocation: As I discussed in my streaming post, using no more than 4 tokens per node can significantly improve streaming performance during scaling operations
2. Memory sizing: Time series workloads benefit from larger key caches but typically don’t need large row caches
3. Storage configuration: Consider the recommendations from my upcoming efficient disk access post to optimize for write-heavy time series patterns

Conclusion: A Solid Foundation for Time Series Success

Getting the fundamentals right is essential for building scalable time series applications with Cassandra. The concepts covered in this post—time bucketing, effective TTL management, and partition key design—form the foundation of successful implementations.

In the next part of this series, I’ll dive deeper into advanced modeling patterns for specific time series use cases, including:

• IoT sensor data with variable measurement frequencies
• Financial time series with real-time aggregation needs
• Monitoring systems with high cardinality dimensions
• Multi-tenant architectures for SaaS applications

I’ll also explore how Cassandra 5.0’s features like Storage Attached Indexing (SAI) and enhanced compression options can further improve time series workloads.

If you’re working with time series data in Cassandra today, I’d love to hear about your experiences. What patterns have worked well for your use cases? What challenges have you encountered? Share your thoughts in the comments below.

Stay tuned for Part 2, where we’ll explore real-world patterns for specific time series applications!