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Why Your Observability Strategy Needs High Cardinality Data

Ayooluwa Isaiah
Updated on May 27, 2024

Traditional monitoring methods, with their reliance on predefined metrics and dashboards, often fail to cope with the dynamic and unpredictable nature of modern systems.

To truly understand and troubleshoot such applications effectively, you need a new approach—one that embraces high cardinality and high dimensionality data.

In this article, we'll examine the concepts of cardinality and dimensionality, how they impact the way you collect, store, and analyze telemetry data to troubleshoot issues faster.

We'll also discuss the associated challenges and how innovative solutions like columnar databases can help you overcome them on your way to achieving true observability.

Let's get started!

What is cardinality?

Cardinality is a mathematical term that deals with the size of sets. It's a way to measure how many elements are in a given set. For example, a set of four numbers (A = {1, 2, 3, 4}) has a cardinality of four.

In the context of databases, cardinality refers to the uniqueness of values contained in a particular column. High cardinality means a column has a high percentage of unique values, while low cardinality means that a high percentage of column values are repeated.

High cardinality vs low cardinality

High cardinality signifies a large number of unique values within a field. Examples of high-cardinality data include user IDs, email addresses, and passport numbers, which are expected to be unique for each individual. Though not entirely unique, fields like first or last names still exhibit high cardinality due to the wide range of possible values.

Conversely, low cardinality indicates a limited number of unique values within a field. Fields such as HTTP methods (GET, POST, etc.), operating systems, or marital status fall into this category. Boolean fields, like email_verified have a very low cardinality, as they can only have two possible values (true or false).

What is dimensionality?

Dimensionality refers to the number of attributes or characteristics that define a data point or event. Each attribute serves as a dimension, offering valuable context and enabling you to dissect and analyze your data from different angles.

Let's consider the following example of a structured log event:

  "host": "SRV001",
  "timestamp": "2024-05-19T14:45:30Z",
  "event": "order",
  "order_id": "987654321",
  "user_id": "123456789",
  "error": {
    "error_code": "E408",
    "error_message": "timedout after 2 minutes",
    "failed_at": "processing_payment",
  "product": {
    "product_id": "D4E5F6",
    "name": "Bluetooth Speaker",
    "quantity": 1,
    "price_per_unit": 45.00
  "payment": {
    "method": "credit_card",
    "transaction_id": "TX987654321DEF"
  "total_cost": 45.00,
  "status": "failed"

In this record, each key/value pair represents a unique dimension. The host dimension tells us where the event occurred, timestamp pinpoints when it happened, event describes the type of event, and so on. With 15 unique attributes, this log has 15 dimensions.

A dimension provides a different lens through which you can examine your data. For instance, you can filter events by the host attribute to see those that occurred on a specific server, or you can correlate errors with specific product IDs using the product_id attribute.

High dimensionality vs low dimensionality

A high-dimensional event has a wealth of attributes or dimensions attached to it. Consider the earlier log record example with its 15 dimensions. It allows for queries like:

  • Finding all failed orders for a specific product and grouping by failure stage,
  • Retrieving recent timeout errors,
  • Listing a specific user's orders within a date range,
  • Grouping failed orders by the host they originated from,
  • And many more.

The more dimensions available, the more versatile and targeted your queries can be. This is crucial when investigating unexpected issues, as it allows for flexible exploration and correlation of data across multiple axes.

In contrast, low dimensionality refers to data with a limited number of attributes. For example, a typical server health check, might only include a timestamp, server ID, and status. While this simplified view provides a general overview of system health, it lacks the depth needed for in-depth troubleshooting.

Essentially, high dimensionality empowers you to slice and dice your data in countless ways, uncovering hidden patterns and relationships. It's a key enabler for quickly identifying and understanding changes in application behavior, even when you don't know exactly what you're looking for.

The relationship between cardinality and dimensionality

Cardinality and dimensionality are complementary concepts that work together to describe the richness and complexity of your data.

A dataset can have high dimensionality (many attributes) and high cardinality within some or all of those dimensions. This means there are many different ways to slice and dice your data, and each slice can contain many unique values.

The role of cardinality and dimensionality in observability

Typical cloud-native deployments consist of hundreds or thousands of components that are loosely coupled, ephemeral, and difficult to reason about.

This has led to an explosion in "unknown unknowns" – unpredictable issues that can be challenging to detect and even harder to diagnose with traditional monitoring.

Without the ability to drill down into individual requests, transactions, or events, find the root cause of issues can become a time-consuming and frustrating process.

Observability offers a solution by allowing ad-hoc exploration of centralized telemetry data to effectively identify and address issues arising from obscure or unexpected combinations of factors without having to ship new code.

But to be truly effective, observability requires highly cardinal and dimensional data. High cardinality provides extensive filtering capabilities to isolate specific events or requests from a sea of data, while high dimensionality helps uncover hidden correlations that would otherwise go unnoticed.

An observability tool must be able to use such data to surface anomalies within the system that could have contributed to something to go wrong and allow you to drill down as needed.

For instance, a handful of users might trigger an error threshold due to a few unusual requests among thousands, such as particularly high-stakes transactions.

Aggregated metrics might mask these individual experiences, but high-cardinality attributes like user or transaction IDs allow for precise identification and investigation of such outlier behavior.

Why high-cardinality metrics don't work

Metrics have inherent limitations when dealing with high cardinality data as most metrics-based monitoring systems are simply not designed to handle the vast number of unique time series generated by high cardinality metrics.

For example, Prometheus recommends keeping the cardinality of the majority of your metrics below 10, and only have a handful that exceed that number due to the increased computational resources required for processing which can add up quickly and escalate costs.

Many systems impose limits on unique labels per metric, and even where there are no strict limits, the cost of adding even one additional metric can be prohibitive.

This often forces organizations to aggregate metrics at higher levels, filter or sample data, or implement retention policies – all of which can compromise the granularity and accuracy of the data, making it harder to pinpoint the root cause of issues.

In essence, metrics lack the granularity and flexibility required for true observability. They are too rigid to present system states in alternate views, hindering the ability to gain a deep understanding of complex systems.

While they remain valuable for tracking overall trends, they fall short when it comes to the detailed, nuanced analysis needed for effective troubleshooting and optimization in high-cardinality environments.

Effective observability with structured logs in columnar databases

We've established that telemetry data that is highly cardinal and dimensional makes it easy to find hidden or elusive patterns in application behavior, which is especially important for cloud-native systems where novel and never-seen-before issues are the norm.

While metrics can be valuable, their limitations become apparent when dealing with high cardinality. To overcome these challenges, shifting from a metrics-based approach to structured logging is essential. Structured logs are designed for machine parsing, and inherently support unlimited cardinality and dimensionality.

But it's not enough to structure your logs. You also need to ensure that they're being sent to an observability platform that uses a database capable of handling the demands of high-cardinality queries and storage.

Since highly dimensional structured logs will have many fields, but queries typically focus on a few specific ones, columnar storage is ideal because it allows having many columns in a table, without paying the cost for unused columns during read query execution time. This makes queries exceptionally fast even when huge volumes of data are involved.

Unlike traditional databases, columnar databases store data by columns rather than rows. They also allow for schema flexibility so you can easily add new fields or modify existing ones without disruptions, allowing the captured data to evolve over time.

At Better Stack, we utilize ClickHouse's columnar database technology technology to store logs in their original format, preserving their diverse data types and structures. You are not constrained by rigid schemas, indexes, or attribute limits, allowing logged data to be as contextually rich as needed.

This approach enables fast and flexible querying without requiring prior knowledge of the log structure. As a result, you can quickly and effortlessly analyze unlimited unique values across various dimensions, even with highly-cardinal data points like IDs.

Debugging with high cardinality data

Imagine being in charge of an e-commerce platform facing a sudden surge in failed payments. While your monitoring dashboard alerts you to this trend, it doesn't reveal the underlying cause. To unravel this mystery, you delve into the captured telemetry data.

By filtering through the traces of failed payments, you discover a concentration of errors originating from the payment gateway service, all linked to a specific "payment declined" error code.

Correlating these traces with relevant logs and comparing them against successful transactions reveal a startling pattern: users from a particular country are disproportionately affected.

Further investigation uncovers that the payment gateway is rejecting these transactions due to a recent policy change in that region.

Armed with this knowledge, you can take decisive action, whether by contacting the payment processor, switching to an alternative, or proactively informing impacted users.

This process highlights the power of high cardinality and dimensionality data in facilitating detective-like investigations into problems, even if they've never been encountered before.

Without capturing fields that revealed the region of users, the errors might appear to be random, and identifying the affected user segment would be considerably more challenging and time-consuming.

Final thoughts

Observability is far more than just a new buzzword for monitoring. It empowers you to dissect your data from any angle to uncover hidden issues and answer questions that couldn't have been anticipated beforehand.

This enhanced capability arises from the use of structured logs in columnar databases instead of relying on metrics, allowing for the removal of predefined schemas and rapid data iteration.

This comprehensive approach is what distinctly differentiates observability from conventional methods.

Are you ready to experience a new approach to observability? Try it out by signing up for a free account today and see the difference firsthand.

Thanks for reading!

Author's avatar
Article by
Ayooluwa Isaiah
Ayo is the Head of Content at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including, Digital Ocean, and CSS-Tricks. When he’s not writing or coding, he loves to travel, bike, and play tennis.
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