Timescale 2025: Scaling Real-Time Analytics in Postgres

Scaling Postgres for Real-Time Analytics on Time-Series Data and Event Data: The Challenge

Real-time analytics for applications requires something different. Traditional databases and analytics systems weren’t built to handle the demands of high-ingest, low-latency queries, and real-time updates—all at scale.

• Postgres slows down as tables grow and queries get more expensive, and updating large indexes becomes costly. 
• Analytical databases are great for fast queries but aren’t built for frequent updates. 
• Hybrid architectures add complexity—sharding Postgres or offloading data introduces lag and increases costs.

Postgres should handle both transactions and real-time analytics in a single system. Over the years, we’ve made that possible—eliminating the need for specialized infrastructure. Now, in 2025, we’re pushing even further—making things faster, more efficient, and proving that Postgres can scale to petabyte workloads—without complex infrastructure.

Monday: Kicking Off Timescale Launch Week

This week, we’re unveiling the next wave of innovations that make Postgres even more powerful for real-time analytics. Each day, we’ll introduce a major breakthrough:

Here’s what’s coming:

• Tuesday: Postgres indexes for columnstore—blazing-fast lookups, inserts, and upserts, even on compressed data.
• Wednesday: Scaling to petabyte workloads—how we dogfood Timescale Cloud, so you can too.
• Thursday: Enabling transition tables on hypertables—optimizing triggers for bulk inserts, updates, and deletes.
• Friday: Revisiting how Timescale’s core architecture already delivers real-time analytics at scale.

Real-time applications need more from a database. This week, we’ll show how Timescale makes Postgres the best choice for application-driven analytics—removing trade-offs between performance, scalability, and simplicity.

Let’s kick things off with a game-changer: Postgres indexes on the columnstore.

Tuesday: Postgres Indexes for Columnstore: 1,185x Faster Lookup Queries, 224x Faster Inserts in TimescaleDB

Postgres indexing, redefined. Until now, columnstores forced a trade-off—fast analytics or fast lookups. With TimescaleDB 2.18, that trade-off is gone.

Compressed data optimized for analytics couldn’t support indexes, meaning queries often required expensive full-table scans. Developers had to choose:

• Fast aggregates but slow point lookups and constraint enforcement.
• Fast inserts but no easy way to find and update specific records efficiently.

With TimescaleDB 2.18, Postgres indexes on columnstore is now in Early Access, where Postgres B-tree and hash indexes work directly on compressed data.

• 1,185x faster point lookups: No need to decompress entire partitions.
• 224.3x faster inserts when checking unique constraints: Enforce data consistency efficiently.
• 2.6x faster upserts: Ensure data integrity without query slowdowns.

Unlike most columnstores—which lack dense indexing altogether—Timescale’s columnstore now supports fast, indexed lookups even on compressed data. 

What does this unlock?

• IIoT systems can now retrieve specific sensor readings in milliseconds—without decompressing full partitions.
• Financial applications can enforce constraints and update historical records efficiently.
• High-ingest systems can backfill and update records at scale—without slowing down query performance.

We broke down exactly how secondary indexes deliver these gains—and why they make real-time analytics and point lookups equally fast in Postgres.

Wednesday: Scale Postgres to 2 PB and 1 T Metrics per Day 

To prove Postgres could handle massive real-time workloads, we put our own technology to the test.

Timescale Insights captures real-time query performance analytics across Timescale Cloud:

• From 350 TB to nearly 2 PB of data stored—with 1.5+ PB seamlessly tiered for cost efficiency.
• From 10 billion to 1 trillion metrics ingested per day—without slowing down inserts or queries.
• 250 trillion total metrics collected—all within a single Timescale instance.

By Wednesday, you’ll see how we push Postgres to its limits—storing 2 PB of data, ingesting 1 trillion metrics per day, all in a single instance. No complex ETL. Just Postgres for real-time analytics at scale.

Thursday: Bulk Triggers for Hypertables? Yes, Finally.

In PostgreSQL 10, transition tables made it possible for statement-level triggers to process all affected rows in bulk during INSERT, UPDATE, or DELETE operations. This was a game-changer for high-ingest workloads—except it didn’t work for TimescaleDB hypertables.

Until now.

With TimescaleDB 2.18, transition tables finally work on hypertables, unlocking bulk-trigger processing for high-ingest workloads. This means:

• Faster change tracking: Track updates across millions of rows without slow, per-row triggers.
• Efficient audit logging: Log bulk changes instantly, avoiding per-row overhead.
• Smarter metadata management: Keep per-ID metadata in sync without expensive row-by-row execution.

This was one of the most requested features for TimescaleDB—and on Thursday, we’ll show you how it can supercharge your real-time analytics workflows.

Friday: Revisiting Our Architectural Innovations for Scaling Postgres

Scaling Postgres for real-time analytics has always required trade-offs:

• Transactional databases handle lots of small transactions with ACID guarantees.
• Analytical databases deliver fast queries but aren’t optimized for real-time updates.
• Hybrid architectures add complexity, forcing developers to stitch together multiple systems.

Timescale eliminates these trade-offs by enhancing Postgres itself—keeping it high-performance, scalable, and analytics-ready.

How? By combining two fundamental innovations:

1. Hypertables automate partitioning (chunking), keeping inserts fast and queries efficient—without the operational headache of manual partitioning.
2. Hypercore—a hybrid storage engine that seamlessly combines row-based and columnar storage, optimizing for high-ingest rates, efficient queries, and real-time updates—without trade-offs.

Beyond storage, Timescale optimizes every step of query execution:

• Partition pruning and chunk skipping eliminate unnecessary partitions before they hit disk.
• SEGMENTBY and ORDERBY colocate related data, reducing random reads and maximizing scan performance.
• Batch filtering and column exclusion ensure only relevant data is read, minimizing I/O.
• Vectorized execution speeds up filtering, decompression, and aggregations for faster queries.

This architecture enables real-time queries on massive datasets, powers incremental rollups with continuous aggregates, and delivers seamless cloud-scale performance through compute-storage decoupling, workload isolation, and cold data tiering.

Here's our white paper, which breaks this down in detail. 

This Is Just the Beginning

By the end of the week, you’ll have a complete picture of how Timescale eliminates bottlenecks, scales to petabytes, and brings the speed of transactional databases to analytics workloads.

But we’re far from done. What’s next?

• Blazing-fast vectorized execution—optimizing every step of query performance.
• Smarter continuous aggregates—for even more efficient rollups and real-time insights.
• Indexing breakthroughs—pushing Postgres analytics further.

And beyond that—seamless data ingestion from S3, Kafka, and real-time event streams, plus expanding Postgres’ role in LLMs, vector search, and AI-driven applications.

Postgres is evolving faster than ever. And this is just the beginning. Watch this space!

→ Spin up Timescale Cloud today and see it in action.