Redefining Database Performance for the Query-Driven Era

We're seeing a seismic shift in the world of database technology. The cloud-based data warehouses that once promised to solve our big data problems are now, ironically, becoming a bottleneck for organizations that actually want to use their data intensively. At m1db, we've been watching this trend closely, and we've built something that we believe represents the next evolution in database technology.

The Problem with Traditional Approaches

Before we dive into what makes m1db different, let's talk about why this matters. The value of data doesn't come from storing it - it comes from using it. A lot. Queries, analysis, training models, anomaly detection, reports, alerts: the more you use your data, the better your chances of discovering new insights. But traditional data warehouses and even modern cloud-based solutions weren't built for this kind of intensive use.

The Problem with The Numbers You’ll See Quoted By Traditional Providers

You’ve probably seen similar performance numbers to the below from databases like Postgres and you may have heard claims from larger providers that they can get within range of some of these numbers. Unlike others, we are not cherry picking best case scenarios here - there are better case scenarios we could engineer - but what you see below are AVERAGE numbers for latency on our system. Our distributed-search based primary datastore is what makes it possible for us to perform like Postgres all the way up to and past Snowflake scales. 

m1db: Built for Query-Intensive Workloads

So, what does a database built for the query-driven era look like? Let me walk you through some key performance metrics that illustrate how m1db is changing the game.

1. Get by ID: ~1 ms

This is the baseline for any system, and we've optimized it to the hilt. In some cases with custom IDs, you might see this creep up to 2-3 ms, but that's still blazing fast.

2. Needle in a Haystack: ~100 ms

Imagine finding all 25-year-old men named Bob who own pickup trucks in specific zip codes, out of a dataset of all people in the US. That's the kind of query we're talking about here. The performance varies a bit with field cardinality and query complexity, but it consistently hovers at or under this 100 ms mark. 

3. Counts and Metrics Within Set: ~100-1000 ms

These are your bread-and-butter analytics queries. Want to count people by make of car within a specific zip code? We've got you covered, typically in under a second.

4. Unfiltered Metrics: 1-10 seconds

These are the big ones - metrics calculated across your entire dataset. Even in worst-case scenarios, we're talking about tens of seconds, not minutes or hours.

Scaling and Capacity: Breaking Down the Numbers

Now, let's talk scale. Our smallest system can handle about 5 million queries per month. A single m1db instance processes 4-6 typical queries per second, plus about 500 get-by-ID lookups. And here's the kicker: query volume scales linearly with replicas. Need more capacity? Just add more replicas.

How Does This Compare to the Competition?

I know what you're thinking: "Sounds great, but how does it stack up against what I'm using now?" Let's break it down:

1. Postgres: We outperform Postgres even on small datasets, and we maintain that edge as your data grows. Plus, our programming model is simpler - no need for partitions, materialized views, or explicit indexes.

2. MongoDB: We match Mongo's performance for its strengths (get-by-ID and simple indexed queries) but vastly outperform it for complex queries. And scaling? With m1db, you just turn on more servers. It's that simple.

3. Redshift (Provisioned): We provide constant-time query performance at all scales. Redshift? Not so much. Their query times increase with dataset size, which is why you don't see Redshift used as an operational back-end.

4. Snowflake/BigQuery/Databricks/Demio/Redshift (Serverless): These platforms can offer good performance, but at what cost? Their on-demand CPU deployment model results in significantly higher per-query costs. If you're doing constant, intensive querying (which, let's face it, you should be if you want to get real value from your data), m1db is going to be much more cost-effective.

The m1db Advantage: Speed, Scale, and Simplicity

Here's what it all boils down to: m1db combines the speed of traditional databases with the scalability of modern data warehouses, all without the need for complex partitioning or caching strategies. We typically outperform Postgres for speed on small datasets and maintain this performance advantage for large datasets where you'd traditionally turn to solutions like Redshift or BigQuery.

Our unique architecture allows us to provide consistent, predictable performance across a wide range of use cases and data sizes. Whether you're dealing with operational workloads or complex analytics, m1db offers the speed and scalability you need to stay ahead in today's data-driven world.

The Future is Query-Driven

At m1db, we believe that the future belongs to organizations that can not just store massive amounts of data, but actually use it - constantly, creatively, and cost-effectively. We've built a database that makes this possible, and we're excited to see what insights and innovations our users uncover.