Timescale 2025: Scaling Real-Time Analytics in Postgres

TL;DR:
Timescale unveils innovations for scaling Postgres for real-time analytics. Key announcements include:

• Secondary indexes for columnstore (700x faster lookups, 30x faster upserts)
• Architectural optimizations that make Postgres analytics-ready without sharding
• Bulk triggers for hypertables
• Proof that Timescale can scale Postgres to handle 2 PB of data and 1 trillion metrics daily in a single instance—eliminating the need to choose between fast inserts and fast queries

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

Postgres is one of the most powerful and flexible databases available, but as real-time analytics workloads grow, traditional approaches start to fail.

• Postgres handles high ingest rates well, but queries slow as datasets grow.
• Analytical databases offer fast queries but struggle with real-time updates and high-ingest workloads.
• Hybrid architectures combine Postgres with an analytics database, but add complexity, increase costs, and can slow insights.

At Timescale, we believe you shouldn’t have to choose. To date, we’ve made Postgres the best database for real-time analytics—eliminating the need for specialized infrastructure.

Now, in 2025, we’re pushing even further—removing bottlenecks, reducing query latency, and proving that Postgres can scale to petabytes of data and trillions of metrics—all in a single instance.

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—removing bottlenecks, accelerating queries, and proving that you can scale Postgres to petabyte workloads without complex infrastructure.

Here’s what’s coming:

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

Let’s kick things off with a game-changer: secondary indexes on compressed data.

Tuesday: Secondary Indexes for Columnstore: 700x Faster Lookup Queries, 30x Faster Upserts 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, that trade-off is gone. Now, Postgres B-tree and hash indexes work directly on compressed data, unlocking:

• 700x faster point lookups: No need to decompress entire partitions.
• 30x faster upserts: Ensure data integrity without query slowdowns.
• 26x faster inserts when checking unique constraints: Enforce data consistency efficiently.

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.

Tomorrow, we’ll break down exactly how secondary indexes deliver these gains—and why they make real-time analytics and point lookups equally fast in Postgres.

Wednesday: Revisiting Our Architectural Innovations for Scaling Postgres

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

• Transactional databases handle high ingest workloads well but struggle with analytics.
• 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—without sharding.

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 dynamically optimizes storage, combining row-based storage for fast ingest and columnar compression for efficient analytics—while supporting updates without costly decompression.

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.

On Wednesday, we’ll release a white paper breaking this down in detail.

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: 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 Friday, 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 sharding. No complex ETL. Just Postgres for real-time analytics at scale.

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. Tomorrow, we dive into secondary indexes—see you then.

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