How to Use Starburst's Caching Features to Accelerate Queries by 10x
Query acceleration is a set of techniques used to speed up data retrieval by storing pre-calculated results or often-accessed data in a faster storage layer. This reduces processing time, lowers costs, and delivers near-instant insights from massive datasets.
Is your data lake slower than you expected? You’ve invested in a modern data stack, built on the promise of interactive, lightning-fast analytics. Yet, your dashboards are sluggish, ad-hoc queries take ages, and your data team is frustrated. Most experts agree that the problem often isn’t the query engine itself, but a misunderstanding of how to properly optimize it.
At DataCouch, we’ve helped dozens of Fortune 500 companies move beyond these performance bottlenecks. The secret to unlocking that promised 10x speed in a platform like Starburst isn’t a single magic setting. It’s about understanding and strategically applying its powerful, multi-layered caching and acceleration architecture.
This guide is your playbook. We’ll walk you through what we call the “Performance Maturity Model”—a step-by-step framework for choosing and implementing the right Starburst caching feature for your specific needs. Whether you’re looking for a quick win or a complete platform overhaul, our Starburst consulting services are designed to guide you through this journey.
The Starburst Performance Maturity Model: A Strategic Framework
Achieving a 10x performance boost in Starburst is a journey, not a single destination. It involves progressing through four distinct layers of acceleration, each with its own use cases, complexity, and impact. Think of it as leveling up your data platform’s performance capabilities.
- Query Result Caching: The simplest, fastest way to get started. Ideal for repetitive queries.
- File System Caching: A broader approach that accelerates access to raw data files in your data lake.
- The Cache Service: An advanced, automated layer for optimizing specific, complex workloads with materialized views.
- Starburst Warp Speed: The ultimate acceleration layer, providing a data warehouse-like experience directly on your object storage.
Let’s break down each layer, so you can understand when and how to use them effectively.
Layer 1: The Quick Win - Query Result Caching
This is the most accessible layer of acceleration in the Starburst platform and your perfect starting point.
What is Query Result Caching?
The mechanism is beautifully simple: when a query is executed, Starburst stores the final result set in a cache for a set amount of time (known as Time-to-Live or TTL). If the exact same query is run again by the same user on the same cluster before the TTL expires, Starburst simply hands back the saved result from the cache. It completely bypasses the expensive process of re-computing the query against the data source.
When Should You Use It?
This feature is a perfect match for any scenario involving repetitive analytics. The most common use case is accelerating Business Intelligence (BI) dashboards and recurring reports. If you have a sales dashboard that ten people refresh every morning with the same filters, query result caching ensures that only the first query does the heavy lifting. The next nine get their results almost instantly.
Getting Started: A Simple Configuration Guide
One of the best things about query result caching is how easy it is to implement.
- In Starburst Galaxy: It’s as simple as flipping a switch. In your cluster configuration, you just toggle on “Cache query results” and set a “Cache reuse period”.
- In Starburst Enterprise (SEP): It requires a few properties in your coordinator’s configuration file. You’ll need to enable it and point it to an S3 bucket where the cached results will be stored.
Here’s a basic example for your SEP configuration:
Properties
results-cache.enabled=true
results-cache.s3.bucket=your-starburst-cache-bucket
results-cache.max-age=1h
The Catch: Know Its Limitations
While powerful, this layer has specific limitations you must understand. The cache is extremely literal.
- Exact Match Required: Any change to the query text, even adding a comment or an extra space, will be treated as a new query and result in a “cache miss”.
- User-Specific: The cache is tied to the user who ran the original query. Another user running the same query will trigger a separate execution and create their own cache entry.
- Size Limits: There is typically a size limit for the results that can be cached (often around 1MB by default), making it unsuitable for queries that return massive datasets.
Despite these limitations, for its intended purpose, query result caching is an incredibly effective “quick win” that can deliver immediate performance improvements.
Layer 2: Broad Acceleration - File System Caching
Once you’ve implemented query result caching, the next step in the maturity model is to accelerate access to the underlying data itself. This is where file system caching comes in.
Why Caching Raw Data Files is a Game-Changer
Instead of caching the final result of a query, this mechanism caches the raw data files (like Parquet or ORC files) that Starburst reads from your object storage (like Amazon S3) during query execution. These files are stored on the fast, local SSDs of the Starburst worker nodes.
A 2024 study from Trino’s open-source community highlighted that this approach dramatically reduces the two biggest bottlenecks in data lake queries: network latency and storage I/O. By keeping frequently accessed data local to the compute cluster, you transform slow network calls into high-speed local disk reads.
Ideal Use Cases: Ad-hoc Analytics on Data Lakes
This layer is perfect for accelerating workloads with repeated access to the same subsets of data, even across many different queries. For example, imagine your analytics team is constantly running different queries against the last quarter’s sales data. After the first query, the underlying Parquet files for that quarter will be cached locally. Every subsequent query that needs that data—regardless of its structure—will be significantly faster.
This makes file system caching far more versatile than query result caching for ad-hoc and exploratory analysis.
How to Implement File System Caching
Configuration is managed at the catalog level in your Starburst properties files. You simply enable caching for a specific data source (like your Hive data lake) and provide a list of local directories on the worker nodes for storage.
Here’s a sample configuration for a Hive catalog:
Properties
# connector.name=hive
fs.cache.enabled=true
fs.cache.directories=/mnt/trino-cache
fs.cache.max-sizes=100GB
fs.cache.ttl=24h
Pro Tip: For best results, always use high-performance, dedicated SSDs on your worker nodes for the cache directories. Avoid using the root disk partition.
The Real ROI: Slashing Cloud Egress Costs
Beyond pure speed, a major benefit of file system caching is cost reduction. Cloud providers charge for data egress (data transferred out of their storage services) and API calls. By caching data locally, you drastically reduce the number of times you need to pull data from object storage, leading to direct and measurable savings on your monthly cloud bill.
Layer 3: Surgical Strikes - The Automated Cache Service
The first two layers are about general-purpose acceleration. The Cache Service is where we get into advanced, surgical optimization for your most critical and complex workloads.
Going Beyond Simple Caching: Materialized Views & Table Scan Redirection
The Cache Service is an advanced framework within Starburst Enterprise that automates the management of pre-computed data, transparently redirecting queries to faster sources. It enables two incredibly powerful features:
- Materialized Views: You can configure the service to automatically run a complex, slow query (e.g., one with multiple joins and heavy aggregations) on a schedule and store the result as a physical table. Starburst’s optimizer is smart enough to then rewrite incoming user queries to use this fast, pre-computed table instead of the original slow one.
- Table Scan Redirection: This allows you to maintain a cached copy of a table from a slow or expensive data source (like a federated PostgreSQL database) inside a fast data lake catalog. When a user queries the original slow table, the Cache Service intercepts the request and transparently serves the data from the fast, cached copy.
Why Most Data Teams Underutilize This Powerful Feature
Here’s a bold statement: if you’re only using basic caching, you’re leaving a massive amount of performance on the table. Many teams are intimidated by the setup of the Cache Service, as it requires deploying a separate service and configuring JSON rule files. However, this one-time investment can permanently solve performance issues for high-value workloads. According to a recent analysis, use cases like offloading queries from production relational databases can see performance gains of over 50x with this method.
A Practical Example: Offloading a Slow Operational Database
Imagine your sales team runs a daily report that joins data from your data lake with customer data from a live production PostgreSQL database. This query slows down the production database and is a constant source of complaints.
With the Cache Service, you can set up a table scan redirection rule. The service will copy the customer table from PostgreSQL into your data lake every hour. Now, when the sales team runs their report, Starburst automatically reads the customer data from the fast, local data lake copy, completely isolating your production database and making the report run dramatically faster.
The Trade-off: Higher Complexity for Pinpoint Optimization
Implementing the Cache Service is a more involved process. It requires deploying the service (either embedded in the coordinator or as a standalone application), connecting it to a backend database, and defining your logic in JSON rule files. This layer is designed for data platform teams who need to solve specific, high-impact performance problems and are willing to invest the time in a more sophisticated configuration.
Layer 4: The Apex Predator - Starburst Warp Speed
At the top of the maturity model sits Starburst Warp Speed, the company’s flagship, proprietary acceleration technology. This isn’t just caching; it’s a complete transformation of how Starburst interacts with your data lake.
What Makes Warp Speed Different?
Warp Speed creates an intelligent indexing and caching fabric over your data in object storage. It goes far beyond caching whole files by transparently building and maintaining:
- Fine-grained indexes: For lightning-fast lookups on WHERE clauses.
- Micro-partitions: Storing data in a more optimized format on high-performance local NVMe SSDs.
- Specialized text search acceleration: To dramatically speed up LIKE predicates.
The result is a system that delivers data warehouse-like interactive query performance directly on your data lake, without needing to move or duplicate your data into a separate system.
Is Warp Speed Right for You? A Candid Assessment
Warp Speed is designed for organizations that have a mission-critical need for ad-hoc, interactive query performance across massive data lakes. If your primary goal is to empower business users and data scientists to explore petabyte-scale datasets in near real-time, Warp Speed is the ultimate solution.
However, this power comes with significant strategic commitments.
The Hidden Cost: Demanding Infrastructure Requirements
Implementing Warp Speed is the most complex of all the layers. It has strict requirements:
- Kubernetes-based Deployment: It must run on EKS, AKS, or GKE.
- Specific Hardware: It requires high-memory worker nodes equipped with multiple local NVMe SSDs.
This represents a considerable investment in both infrastructure cost and operational complexity. It’s a strategic decision that should be made when the business value of extreme, interactive performance justifies the cost.
The Decision Framework: Your Starburst Acceleration Cheat Sheet
Choosing the right strategy can be daunting. To simplify the process, we’ve created this comparison table to help you make an informed decision based on your specific needs.
| Dimension | Query Result Caching | File System Caching | Cache Service (Materialized Views)s | Starburst Warp Speed |
|---|---|---|---|---|
| Primary Use Casee | BI Dashboards, identical repeating queries | Ad-hoc queries on frequently accessed data subsets | Optimizing specific, complex, high-value queries | Platform-wide interactive query performance |
| Granularity | Entire query result set | Data files/objects (e.g., Parquet files) | Pre-aggregated/joined tables (Views) | Indexed data blocks & row groups |
| Configuration Complexity | Low (Toggle/few properties) | Medium (Catalog properties, worker storage) | High (Separate service, DB, JSON rules) | Very High (K8s, specific node types, NVMe) |
| Performance Impact | High (for cache hits), Zero (for misses) | High (broad impact on I/O) | Very High (for targeted queries) | Extreme (for interactive workloads) |
| Infrastructure Dependency | S3 Bucket | Local disk/SSD on workers | Backend RDBMS, optional separate compute | Kubernetes (EKS/AKS/GKE), High-mem nodes, NVMe SSDs |
| Data Freshness | Stale (based on TTL) | Stale (based on TTL) | Managed (refreshed on a schedule) | Near real-time (indexes updated) |
Final Words: Which Caching Strategy Will You Choose in 2025?
Achieving 10x query acceleration with Starburst is not about finding a single secret setting. It’s about making intelligent, informed architectural choices. By understanding the Performance Maturity Model, you can plot a clear path forward:
- Start today with Query Result Caching to get immediate relief for your BI dashboards.
- Next quarter, implement File System Caching to boost ad-hoc query performance and lower your cloud storage costs.
- As your platform matures, identify your most painful, complex queries and surgically optimize them with the Cache Service.
- For your ultimate goal, evaluate Warp Speed as a strategic investment to deliver a truly interactive data lake experience.
Navigating these layers requires expertise. If you’re ready to move beyond slow queries and unlock the full potential of your data platform, let’s talk. Our Starburst consulting services provide hands-on guidance and deep technical knowledge to help you implement the right strategy, tailored to your unique environment and goals.
