Google Cloud: Investing in the future of PostgreSQL

by Dilip Kumar, Cloud SQL - Postgres

At Google Cloud, we are deeply committed to open source, and PostgreSQL is a cornerstone of our managed database offerings, including Cloud SQL & AlloyDB.

Continuing our work with the PostgreSQL community, we've been contributing to the core engine and participating in the patch review process. Below is a summary of that technical activity, highlighting our efforts to enhance the performance, stability, and resilience of the upstream project. By strengthening these core capabilities, we aim to drive innovation that benefits the entire global PostgreSQL ecosystem and its diverse user base.

Our investments in PostgreSQL logical replication aim to unlock critical capabilities for all users. By enhancing conflict detection, we are paving the way for robust active-active replication setups, increasing write scalability and high availability. We are also focused on expanding logical replication to cover missing objects. This is key to enabling major version upgrades with minimal downtime, offering a more flexible alternative to pg_upgrade. Furthermore, our ongoing contributions to bug fixes are dedicated to improving the overall stability and resilience of PostgreSQL for everyone in the community.

Technical contributions: July 2025 – December 2025

The following sections detail technical enhancements and bug fixes contributed to the PostgreSQL open source project between July 2025 and December 2025. Primary engineering efforts were dedicated to advancing logical replication toward active-active capabilities, implementing missing features, optimizing pg_upgrade, and fixing bugs.

Logical Replication Enhancements

Logical replication is a critical feature of PostgreSQL enabling capabilities like near zero down time, major version upgrades, selective replication, active-active replication. We have been working towards closing some of the key gaps.

Automatic Conflict Detection

Active-active replication is a mechanism for increasing PostgreSQL write scalability. One of the most significant hurdles for active-active PostgreSQL setups is handling row-level conflicts when the same data is modified on two different nodes. Historically, these conflicts could stall replication, requiring manual intervention.

In this cycle, the community committed Automatic Conflict Detection which is the first phase of Automatic Conflict Detection and Resolution. This foundation allows the replication worker to automatically detect when an incoming change (Insert, Update, or Delete) conflicts with the local state.

Contributors: Dilip Kumar helped by performing code and design reviews. He is currently advancing the project's second phase, focusing on implementing conflict logging into a dedicated log table.

Logical replication of sequences

Until recently, logical replication in PostgreSQL was primarily limited to table data. Sequences did not synchronize automatically. This meant that during a migration or a major version upgrade, DBAs had to manually sync sequence values to prevent "duplicate key" errors on the new primary node. Since many databases rely on sequences, this was a significant hurdle for logical replication.

Contributors: Dilip Kumar helped by performing code and design reviews.

Drop subscription deadlock

The DROP SUBSCRIPTION command previously held an exclusive lock while connecting to the publisher to delete a replication slot.

If the publisher was a new database on the same server, the connection process would stall while trying to access that same locked catalog.

This conflict created a "self-deadlock," where the command was essentially waiting for itself to finish.

Contributors: Dilip Kumar analyzed and authored the fix.

Upgrade Resilience

Operational ease of use and friction-less upgrades are important to PostgreSQL users. We have been working on improving the upgrade experience.

pg_upgrade optimization for Large Objects

For databases with massive volumes of Large Objects, upgrades could previously span several days. This bottleneck is resolved by exporting the underlying data table directly rather than executing individual Large Object commands, resulting in an upgrade process that is several orders of magnitude faster.

Contributors: Hannu Krosing, Nitin Motiani and, Saurabh Uttam, highlighted the severity of the issue, proposed the initial fix and actively drove it to the resolution.

Prevent logical slot invalidation during upgrade:

Upgrade to PG17 fails if max_slot_wal_keep_size is not set to -1. This fix improves pg_upgrade's resilience, eliminating the need for users to manually set max_slot_wal_keep_size to -1. The server now automatically retains the necessary WAL data for upgrading logical replication slots, simplifying the upgrade process and reducing the risk of errors.

Contributors: Dilip Kumar analyzed and authored the fix.

pg_upgrade NOT NULL constraint related bug fix

A bug in pg_dump previously failed to preserve non-inherited NOT NULL constraints on inherited columns during upgrades from version 17 or older.

The fix updates the underlying query to ensure these specific schema constraints are correctly identified and migrated during the pg_upgrade process.

Contributors: Dilip Kumar analyzed and authored the fix.

Miscellaneous Bug Fixes

We continue to contribute bug fixes to help improve the stability and quality of PostgreSQL.

Make pgstattuple more robust about empty or invalid index pages

pgstattuple is a PostgreSQL extension for analyzing the physical storage of tables and indexes at the row (tuple) level, to determine whether a table is in need of maintenance. However, pgstattuple would raise errors with empty or invalid index pages in hash and gist code. This bug handles the empty and invalid index pages to make pgstattuple more robust.

Contributors: Nitin Motiani and Dilip Kumar, participated as author and reviewer.

Loading extension from different path

A bug incorrectly stripped the prefix from nested module paths when dynamically loading shared library files. This caused libraries in subdirectories to fail to load. The bug fix ensures the prefix is only removed for simple filenames, allowing the dynamic library expander to correctly find nested paths

Contributors: Dilip Kumar, reported and co-authored the fix for this bug.

WAL flush logic hardening

XLogFlush() and XLogNeedsFlush() are internal PostgreSQL functions that ensure log records are written to the WAL to ensure durability. In certain edge cases, like the end-of-recovery checkpoint, the functions relied on inconsistent criteria to decide which code path to follow. This inconsistency posed a risk for upcoming features i.e. Asynchronous I/O for writes that require XLogNeedsFlush() to work reliably.

Contributors: Dilip Kumar, co-authored the fix for this bug.

Major Features in Development

Beyond our recent commits, the team is actively working on several high-impact proposals to further strengthen the PostgreSQL ecosystem.

• Conflict Log Table for Detection: Dilip Kumar is developing a proposal for a conflict log table designed to offer a queryable, structured record of all logical replication conflicts. This feature would include a configuration option to determine whether conflict details are recorded in the history table, server logs, or both.

• Dumping tables data in multiple chunks in pg_dump: Hannu Krosing is working on this feature, this enables parallel workers for single, large tables (terabytes in size) to saturate hardware limits and speed up exports.

• Adding pg_dump flag for parallel export to pipes: Nitin Motiani is working on this feature. This introduces a flag which allows the user to provide pipe commands while doing parallel export/import from pg_dump/pg_restore (in directory format).

Leadership

Beyond code, our team supports the ecosystem through community leadership. We are pleased to share that Dilip Kumar has been selected for the PGConf.dev 2026 Program Committee to help shape the project's premier developer conference.

Community Roadmap: Your Feedback Matters

We encourage you to utilize the comments area to propose new capabilities or refinements you wish to see in future iterations, and to identify key areas where the PostgreSQL open-source community should focus its investments.

Acknowledgement

We want to thank our open source contributors for their dedication to improving the upstream project.

Dilip Kumar: PostgreSQL significant contributor

Hannu Krosing: PostgreSQL significant contributor

Nitin Motiani: Contributing features and bug fixes

Saurabh Uttam: Contributing bug fixes

We also extend our sincere gratitude to the wider PostgreSQL open source members, especially the committers and reviewers, for their guidance, reviews, and for collaborating with us to make PostgreSQL the most advanced open source database in the world.

Advanced TPU optimization with XProf: Continuous profiling, utilization insights, and LLO bundles

by Yogesh SY, AI Infra @ Google

Advanced TPU optimization with XProf: Continuous profiling, utilization insights, and LLO bundles

In our previous post, we introduced the updated XProf and the Cloud Diagnostics XProf library, which are designed to help developers identify model bottlenecks and optimize memory usage. As machine learning workloads on TPUs continue to grow in complexity—spanning both massive training runs and large-scale inference—developers require even deeper visibility into how their code interacts with the underlying hardware.
Today, we are exploring three advanced capabilities designed to provide "flight recorder" visibility and instruction-level insights: Continuous Profiling Snapshots, the Utilization Viewer, and LLO Bundle Visualization.

Continuous Profiling Snapshots: The "Flight Recorder" for ML

Standard profiling often relies on "sampling mode," where users manually trigger high-fidelity traces for short, predefined durations. While effective for general optimization, this traditional approach can miss transient anomalies, intermittent stragglers, or unexpected performance regressions that occur during long-running training jobs.
To address this visibility gap, XProf is introducing Continuous Profiling Snapshots. This feature functions as an "always-on" flight recorder for your TPU workloads.
How it works: Continuous profiling snapshots (Google Colab) operates quietly in the background with minimal system overhead (approximately 7µs per packet CPU overhead). It utilizes a host-side circular buffer of roughly 2GB to seamlessly retain the last ~90 seconds of performance data. This architecture allows developers to snapshot performance data programmatically precisely when an anomaly occurs, bypassing the overhead and unpredictability of traditional one-shot profiling.

Figure 1: Traditional trace capturing without Continuous Profiling Snapshots.

Figure 2: Comprehensive context captured via Continuous Profiling Snapshots.

Key technical features include:

• Circular Buffer Management: Continuously holds recent trace data to ensure you can capture the exact moments leading up to an anomaly or regression.
• Out-of-band State Tracking: A lightweight service polls hardware registers for P-state (voltage and frequency) and trace-drop counters, ensuring the snapshot contains the necessary environmental context for accurate analysis.
• Context Reconstruction: The system safely decouples state capture from the trace stream. This ensures that any arbitrary snapshot retains the ground truth required for precise, actionable debugging.

Visualizing Hardware Efficiency with the Utilization Viewer

Raw performance counters are powerful, but interpreting thousands of raw hardware metrics can be a daunting, time-consuming process. The new Utilization Viewer bridges the gap between raw data streams and actionable optimization strategies.
This tool translates raw performance counter values into easily understandable utilization percentages for specific hardware components, such as the TensorCore (TC), SparseCore (SC), and High Bandwidth Memory (HBM).

Figure: Raw Performance Counter
Figure 3: Deriving actionable insights from raw performance counters.

From Counters to Insights: Instead of requiring developers to manually analyze a raw list of event counts, the Utilization Viewer automatically derives high-level metrics. For example, it can translate raw bus activity into a clear utilization percentage (e.g., displaying an average MXU bus utilization of 7.3%). This immediate clarity allows you to determine at a glance whether your model is compute-bound or memory-bound.

Figure 4: Perf Counters Visualization in Utilization Viewer

Inspecting the Metal: Low-Level Operations (LLO) Bundles

For advanced users and kernel developers utilizing Pallas, we are now exposing Low-Level Operations (LLO) bundle data. LLO bundles represent the specific machine instructions issued to the TPU's functional units during every clock cycle.

This feature is critical for "Instruction Scheduling" verification—ensuring that the compiler is honoring your programming intentions and correctly re-ordering instructions to maximize hardware performance.

New Visualizations via Trace View Integration: You can now visualize LLO bundles directly within the trace viewer. Through dynamic instrumentation, XProf inserts traces exactly when a bundle executes. This provides exact execution times and block utilization metrics, rather than relying on static compiler estimates.

Why it matters: Accessing this level of granularity enables hyper-specific bottleneck analysis. For instance, developers can now identify idle cycles within the Matrix Multiplication Unit (MXU) pipeline, making it easier to spot and resolve latency between vmatmul and vpop instructions.

Conclusion

Whether you are trying to capture a fleeting performance regression with Continuous Profiling, verifying kernel efficiency with LLO Bundles, or assessing overall hardware saturation with the Utilization Viewer, these new features bring internal-grade Google tooling directly to the open-source community. These tools are engineered to provide the absolute transparency required to optimize high-scale ML workloads.

Get started by checking out the updated resources:

• XProf GitHub Repository: https://github.com/openxla/XProf
• Official Documentation: https://openxla.org/xprof

Open Source, Open Doors, Apply Now for Google Summer of Code!

by Stephanie Taylor, Mary Radomile & Lucila Ortíz, GSoC Program Admins

Join Google Summer of Code (GSoC) and start contributing to the world of open source development! Applications for GSoC are open from now - March 31, 2026 at 18:00 UTC.

Google Summer of Code is celebrating its 22nd year in 2026! GSoC started back in 2005 and has brought over 22,000 new contributors from 123 countries into the open source community. This is an exciting opportunity for students and beginners to open source (18+) to gain real-world experience during the summer. You will spend 12+ weeks coding, learning about open source development, and earn a stipend under the guidance of experienced mentors.

Apply and get started!

• First things first. Read the Contributor Guide and Advice for people applying for GSoC for application basics.
• Elevate your proposal! Review the Writing a proposal doc written by former contributors and the Guidance for GSoC Contributors using AI tooling in GSoC 2026.
• Read the Program Rules, FAQ, and join us in our Discord Channel to connect with the GSoC community.
• Explore the 184 mentoring organizations, find a couple that align with your interests/skills and reach out immediately by using their preferred contact methods listed on the GSoC site. Do not email mentors directly unless explicitly told to do so in their instructions.
• Watch our Intro to GSoC video, as well as the GSoC Org Highlight videos and Community Talks Series to get inspired about projects that contributors have worked on in the past.

Please remember that mentors are volunteers and they are being inundated with hundreds of requests from interested participants. It may take time for them to respond to you. Follow their Contributor Guidance instructions exactly. Do not just start submitting PRs without reading their guidance section first.

Complete your registration and submit your project proposals on the GSoC site before the deadline on Tuesday, March 31, 2026 at 18:00 UTC.

We wish all our applicants the best of luck!

OpenTitan shipping in production

by Cyrus Stoller & Miguel Osorio, OpenTitan

Last year, we shared the exciting news that fabrication of production OpenTitan silicon had begun. Today, we're proud to announce that OpenTitan® is now shipping in commercially available Chromebooks.

The first OpenTitan part is being produced by Nuvoton, a leader in silicon security.

What is OpenTitan?

Over the past seven years, Google has worked with the open source communities to build OpenTitan, the first open source silicon Root of Trust (RoT). The RoT is the foundation upon which all other security properties of a device are derived, and anchoring this in silicon provides the strongest possible security guarantees that the code being executed is authorized and verified.

The OpenTitan project and its community are actively supported and maintained by lowRISC C.I.C., an independent non-profit.

OpenTitan provides the community with a high-quality, low-cost, commoditized hardware RoT that can be used across the Google ecosystem and also facilitates the broader adoption of Google-endorsed security features across the industry. Because OpenTitan is open source, you can choose to buy it from a commercial partner or manufacture it yourself based on your use case. In any of these scenarios, you can review and test OpenTitan's capabilities with a degree of transparency never afforded before in security silicon. This allows optimization for the use case at hand, whether it is having multiple reliable suppliers or ensuring the complete end-to-end control of the manufacturing process.

With OpenTitan, we are pushing the boundaries of what can be expected from a silicon RoT. For example, OpenTitan is the first commercially available open source RoT to support post-quantum cryptography (PQC) secure boot based on SLH-DSA. This helps future proof the security posture of these devices against potential adversaries with the capability to break classical public-key cryptography (e.g., RSA) via quantum computing. In addition, by applying commercial-grade design verification (DV) and top-level testing to an open source design, we have pushed for the highest quality while still allowing these chips to be transparent and independently verifiable. An added advantage of this approach is that we expect the high quality IP developed for OpenTitan to be re-usable in other projects going forward.

In addition to delivering this first instance of OpenTitan silicon as a product, we are proud of the processes that we have collaboratively developed along the way. In particular, both individual IP blocks and the top-level Earl Grey design have functional and code coverage above 90%—to the highest industry standards—with 40k+ tests running nightly. Regressions are caught and resolved quickly, ensuring design quality is maintained over the long term. Ownership transfer gives confidence that the silicon is working for you and helps to move away from co-signing so that you are in full control of your own update schedule. And since any IP is of little value without the ability to navigate and deploy it, we've prioritized thorough and accurate documentation, together with onboarding materials to streamline welcoming new developers to the project.

With lowRISC CIC and in collaboration with our OpenTitan partners, we pioneered open source security silicon development. While challenges are expected when doing something for the first time, the benefits of working in the open source have been clear: fast and efficient cross-organizational collaboration, retention of expertise regardless of employer, shared maintenance burdens, and high levels of academic research engagement.

What's next?

Firstly, bringup to deploy OpenTitan in Google's datacenters is underway and expected to land later this year.

Secondly, while we're thrilled about the advantages that this first generation OpenTitan part brings to Google's security posture, we have more on our roadmap, and have already begun work on a second generation part that will support lattice-based PQC (e.g., ML-DSA and ML-KEM) for secure boot and attestation. Stay tuned – more info on this coming soon!

Thirdly, OpenTitan started with the security use case because it is the hardest to get right. Having successfully demonstrated that we are able to deliver secure open silicon, we're confident that the same methodology can be used to develop additional open source designs targeting a wide range of use cases (whether the focus is on security, safety, or elsewhere). We're excited to see re-use of IP that was developed for OpenTitan being adapted for Caliptra, a RoT block that can be integrated into datacenter-class SoCs.

Getting Involved

OpenTitan shipping in production is a defining milestone for us and all contributors to the project. We're excited to see more open source silicon developed for commercial use cases in the future, and to see this ecosystem grow with lowRISC's introduction of new membership tiers.

As the following metrics show (baselined from the project's public launch in 2019), the OpenTitan community is rapidly growing:

• Over ten times the number of commits at launch: from 2,500 to over 29,200.
• 275+ contributors to the code base
• 3.2k Github stars

If you are interested in learning more, contributing to OpenTitan, or using OpenTitan IP in one of your projects, visit the open source GitHub repository or reach out to the OpenTitan team.

Announcing CEL-expr-python: the Common Expression Language in Python, now open source

by Olena Huang, CEL (Common Expression Language) team

We're excited to announce the open source release of CEL-expr-python, a Python implementation of the Common Expression Language (CEL)! CEL (cel.dev) is a powerful, non-Turing complete expression language designed for simplicity, speed, safety, and portability. CEL is designed to be embedded in an application, and you can use CEL to make decisions, validate data, or apply rules based on the information your application has.

What is CEL-expr-python?

CEL-expr-python provides a native Python API for compiling and evaluating CEL expressions that's maintained by the CEL team. We'd like to acknowledge the fantastic work already done by the open source communities around support for CEL in Python, and look forward to your contributions to help us further enrich the CEL ecosystem.

The CEL team has chosen to develop CEL-expr-python by wrapping our official C++ implementation to ensure maximum consistency with CEL semantics while enabling Python users to extend and enrich the experience on top of this production-ready core in Python directly. Additionally, new features and optimizations implemented in CEL C++ will automatically and immediately become available in CEL-expr-python.

Who is it for?

If you're working on a Python project that needs to:

• Evaluate expressions defined dynamically (e.g., loaded from a database, configuration, or user input).
• Implement and enforce policies in a clear, concise, and secure manner.
• Validate data against a set of rules.

...then CEL-expr-python is for you!

Why use CEL-expr-python?

CEL has become a prevalent technology for applications like policy enforcement, data validation, and dynamic configuration. CEL-expr-python allows Python developers to leverage the same benefits, including:

• Safety: CEL expressions are side-effect free and terminate guaranteed.
• Speed: Designed for efficient evaluation.
• Portability: Expressions are language-agnostic.
• Familiarity: Builds upon established CEL concepts.

With CEL-expr-python, you can now seamlessly integrate this technology within your Python stack.

Get Started!

Check out the CEL-expr-python Repository here: https://github.com/cel-expr/cel-python

We're thrilled to bring CEL-expr-python to the open source communities and can't wait to see what you build with it!

Here's a code snippet demonstrating how to initialize CEL-expr-python and evaluate an expression.

from cel_expr_python import cel

# Define variables
cel_env = cel.NewEnv(variables={"who": cel.Type.STRING})
expr = cel_env.compile("'Hello, ' + who + '!'")

# Evaluate and print the compiled expression
print(expr.eval(data={"who": "World"})))  // Hello, World!

For a more in-depth tutorial, check out our codelab here: https://github.com/cel-expr/cel-python/blob/main/codelab/index.lab.md

The CEL-expr-python repository will be initially available as read-only. We encourage you to try it out in your projects and share your experiences. Feel free to leave feedback in our github issue queue, as we are eager to hear your feedback and will be working promptly to address any issues or suggestions.

While we are not accepting external contributions at this moment, we are committed to building a community around CEL-expr-python and plan to open up for contributions in the future. Stay tuned for updates.