Your Time-Series Data Is Drowning Your PostgreSQL Instance. There's a Better Way.
Time-series data—from IoT sensors, financial trades, or application metrics—is one of the fastest-growing and most challenging data workloads. A standard PostgreSQL table will quickly buckle under the relentless write volume and the complex time-based queries required for analysis. This is the problem TimescaleDB was built to solve. As a powerful extension to PostgreSQL, it provides automatic partitioning, improved query performance, and specialized functions that make managing time-series data at scale not just possible, but efficient.
But this power is not automatic. An engineer who approaches a TimescaleDB hypertable as if it were a normal PostgreSQL table will fail to unlock its potential. They will create inefficient chunks, write slow queries that don't leverage time-based optimizations, and fail to use features like continuous aggregates and data retention policies. You get the complexity of a specialized database with none of the performance benefits.
An engineer who knows how to create an extension is not a TimescaleDB expert. An expert understands how hypertables and chunks work under the hood. They can design a schema that is optimized for both fast ingest and fast queries. They know how to use TimescaleDB's specialized functions to perform complex time-series analysis efficiently. This playbook explains how Axiom Cortex finds the engineers who have this deep, practical expertise.
Traditional Vetting and Vendor Limitations
A nearshore vendor sees "PostgreSQL" on a résumé and assumes that "TimescaleDB" is just a minor addition. The interview focuses on generic SQL questions. This superficial approach completely fails to test for the critical, time-series-specific skills needed to build and operate a high-performance IoT or monitoring platform.
The predictable and painful results of this flawed vetting are common:
- Slow Time-Based Queries: A dashboard that shows data from the last 24 hours is slow because the query is scanning months of data. The developer doesn't understand how TimescaleDB's time-based partitioning can be used to dramatically prune the data that needs to be read.
- Inefficient Continuous Aggregates: The team tries to build a summary table manually with a complex ETL job. They are completely unaware of TimescaleDB's continuous aggregates feature, which is designed to do this automatically and efficiently.
- Storage Bloat: The database size grows uncontrollably because the team has no strategy for downsampling or deleting old data. They don't know how to implement data retention policies or use compression.
- The "Wrong Tool for the Job" Anti-Pattern: The developer uses a generic `GROUP BY` on a massive hypertable instead of a more efficient time-series-specific function like `time_bucket()`, resulting in slow and resource-intensive queries.
How Axiom Cortex Evaluates TimescaleDB Developers
Axiom Cortex is designed to find the engineers who think in terms of time, chunks, and continuous aggregation. We test for the practical skills that are essential for building high-performance time-series applications with TimescaleDB. We evaluate candidates across four critical dimensions.
Dimension 1: Time-Series Data Modeling
This is the foundation of a performant TimescaleDB application. This dimension tests a candidate's ability to design a schema that is optimized for time-series workloads.
We provide a time-series use case (e.g., "ingest data from a fleet of IoT devices") and evaluate their ability to:
- Design a Hypertable: Can they correctly identify the time column and the partitioning columns to create an efficient hypertable? Can they explain how chunking works?
- Understand Wide vs. Narrow Tables: Can they discuss the trade-offs between a "wide" table with many columns for different metrics versus a "narrow" table with a metric type and a value column?
Dimension 2: Query Optimization for Time-Series
This dimension tests a candidate's ability to write fast, efficient queries that leverage TimescaleDB's unique capabilities.
We present a query problem and evaluate if they can:
- Use Time-Series Specific Functions: A high-scoring candidate will immediately use functions like `time_bucket()`, `first()`, and `last()` to write clean and efficient time-based aggregations.
- Leverage Chunk Pruning: Can they write a query with a `WHERE` clause on the time column that allows TimescaleDB's query planner to effectively prune the chunks it needs to scan?
Dimension 3: Data Lifecycle Management
Time-series data grows relentlessly. An elite engineer must have a strategy for managing this data over its lifecycle.
We evaluate their knowledge of:
- Continuous Aggregates: Can they design and implement a continuous aggregate to pre-compute summary data for dashboards and long-range queries?
- Data Retention Policies: Can they set up a policy to automatically drop or downsample old data that is no longer needed at its original granularity?
- Compression: Do they know how to enable and configure TimescaleDB's columnar compression to reduce storage costs?
From a Slow Log Table to a High-Performance Time-Series Engine
When you staff your team with engineers who have passed the TimescaleDB Axiom Cortex assessment, you are investing in a team that can build truly scalable and performant time-series applications. They will not just treat it as a big PostgreSQL table; they will leverage its specialized features to build a system that can handle massive ingest volumes and deliver fast analytical queries, something that is nearly impossible with a standard relational database.