A Crunchy to Percona PostgreSQL migration is more straightforward than most cross-operator moves on Kubernetes, because the Percona PostgreSQL Operator is a hard fork of the Crunchy Data PostgreSQL Operator. Same Patroni HA, same pgBackRest backups, same overall CRD shape. This post walks through the safest of the three migration paths: a standby cluster method … Continued

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For decades, financial institutions have relied on proprietary databases to power everything from customer transactions to real-time risk engines. But the pressures facing banks, fintechs, and payment providers have changed. Even minutes of downtime can trigger customer loss. 91% of enterprises report costs over $300,000 per hour, with 44% saying it can exceed $1 million. … Continued

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Overview Servers Tested: MySQL 9.7.0 (PGO-enabled build released by Oracle) MySQL 9.7.0 Non-PGO (built without Profile-Guided Optimization — see BUILD.md) Tier Configurations: Tier 2G: 2GB InnoDB buffer pool Tier 12G: 12GB InnoDB buffer pool Tier 32G: 32GB InnoDB buffer pool   View Results 📊 Interactive Reports The benchmark reports are available as interactive HTML pages … Continued

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Chess is a lot trickier than it looks. It has so many rules: castling, en passant, pawn promotion, pinning, the discovered check, and the deadlock case of stalemate.

It is a concurrent system, but with a very specific kind of concurrency: interleaved execution. More specifically, taking turns: white, then black, then white.

You know what we do with concurrent systems here? Here we model them, and we distill their invariants.

Here is some setup definitions first.

In a CS or math paper, if you write "Section 2: Model and Problem" well enough, the rest of the paper writes itself. With this setup you can sort of see what the actions will be.

In fact, forget about the actions. Let's look at some invariants.

Invariants

When deriving invariants we ask: what must always be true? I find it useful to split the safety invariants into two camps: state invariants (which are predicates over a single state) and transition invariants (which are predicates over a step). The transition invariants are not as commonly used as state invariants, but they can be very helpful, especially when you are reasoning about transitions of a system.

State invariants

TypeOK says every variable lives in the right space. It is boring, but it has caught more bugs than I would like to admit. OneKingPerColor and BothKingsOnBoard are also sanity checks. 

TurnParity is the first interesting one. It ties two state variables together: WHITE moves on even moves, BLACK on odd. The MakeMove action satisfies this TurnParity.

PreviousPlayerNotInCheck restates the rule that "you must end your turn not in check" as "look back: the player who just moved is not in check". NotBothInCheck is a corollary.

Transition invariants

These are predicates over a <<state, next-state>> pair, written with the bracketed form: [][P]_vars. They express how things change with constraints.  The notation is simple: x is the value of the variable x in this state, and x' denotes the value in the next-state.

MoveCountStrictlyIncreases and TurnAlternates say each step increments the move count with the colors flipping. If a transition ever messes this up, something has gone wrong.

PieceCountNonIncreasing rules out pieces appearing out of thin air. SingleCapturePerMove tightens this: at most one piece disappears per step. ExactlyTwoSquaresChange is the strongest here. It says precisely two squares change per move, the source (now empty) and the destination (now holding the moving piece). 

Haha, yes, this is a model of the basic chess rules only. A useful exercise here is to consider which of these invariants survive when we add castling, pawns, en passant?

ExactlyTwoSquaresChange gets violated when we add castling: four squares change in one move. Similarly, en passant captures a piece not on the destination square, so three squares change.

PieceCountNonIncreasing survives pawn promotion (when a pawn becomes a queen, the count is unchanged).

TL;DR: A bug in MongoDB’s time-series collection code allows a user with the standard readWrite role to corrupt memory within the mongod process. Best case: your database crashes, and you spend the night writing a postmortem. Worst case: an attacker is running their code as mongod, with the same access to your data that the … Continued

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Takeaway: Adding foreign keys require schema modification locks on every table involved. This can cause deadlocks on busy systems. Schema modification locks (SCH-M) are taken by DDL (Data Definition Language) statements like CREATE/ALTER/DROP. Schema stability locks (SCH-S) are taken by DML (Data Manipulation Language) statements like INSERT/UPDATE/DELETE. Those two types of locks are incompatible. Meaning, […]

This article explains how to manually migrate hash slots in Valkey/Redis clusters to expand your deployment with minimal disruption to availability. Note: Valkey 9.0 introduces the Atomic Slot Migration (ASM) feature, which significantly improves migration speed (up to 9.52 times faster) and reliability, while reducing migration complexity. So you should use ASM instead if you … Continued

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Choosing an open source PostgreSQL operator for Kubernetes used to be a question about features and community size. In 2026, it has become a question about licensing posture, image distribution, and whether the project you pick today will still be operationally open in three years. This is part 1 of a 3-part series on running … Continued

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When the future of pgBackRest suddenly became uncertain, the PostgreSQL ecosystem reacted quickly. At Percona, we believed the most important question was not: what replaces it? but: how do we ensure pgBackRest remains healthy, sustainable, and open for everyone? That distinction matters. pgBackRest is critical infrastructure used by enterprises around the world to protect some … Continued

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This is part of our series going through OSTEP book chapters. The OSTEP textbook is freely available at Remzi's website if you like to follow along.

This chapter extends the CPU virtualization playbook to memory. It's the same recipe: let the program run directly on the hardware, but interpose at carefully chosen points so the OS retains control. For memory, this happens at every memory access. Every load, store, and instruction fetch gets translated by hardware from a virtual to a physical address.

The mechanism here is called dynamic relocation, dating to the late 1950s. The base register holds the physical address where the process's address space starts, the bounds register holds its size. On every memory reference the hardware adds base and checks against bounds. If the address is out of range, the CPU raises an exception, and the OS kills the offender.

This takes collaboration between hardware and the OS. Hardware provides privileged mode, the base/bounds registers, translation circuitry, exception generation, and privileged instructions to update the registers. The OS provides memory allocation (a free list, in the simplest case), base/bounds management across context switches, and the exception handlers themselves.

Because there is only one base/bounds pair per CPU, the OS must save and restore them in the process control block(PCB). This means that while a process is descheduled, the OS can freely move its address space and then update the saved base. The process wakes up oblivious to this, hence the name dynamic relocation.

The chapter is transparent about what base-and-bounds gets wrong. The relocated process gets a fixed-size slot, but its stack and heap occupy only a small fraction of it, which means that the space in between causes internal fragmentation. With every process getting the same fat slot regardless of actual footprint, the physical memory fills up quickly. The segmentation discussion, coming next chapter, aims to fix this.