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Deep, recent reads ranked by depth and freshness.

PostgreSQL Observability Databases Architecture Clear all

01Cloudflare · architecture
Origin Range Requests: Serving large files without range-request tuning
Cloudflare replaced a legacy, fixed-size range caching design that suffered from bandwidth waste and memory pressure with a Pingora-based chunk coordinator. The new implementation dynamically combines cached chunks and aligned origin fetches to serve range requests accurately without manual tuning. This approach cut p99 time to first byte in half for R2 storage and reduced tiered cache traffic by six percent.
DEPTH ■■■■■8 min read2 days ago
Databases Performance Redis
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02PlanetScale · architecture
Shard design doesn't start from your tables
Designing a sharded Postgres architecture requires grouping tables around shared query keys rather than starting from table structures alone. The post details how to configure shard groups, reference tables, SQL expressions in shard indexes, and global secondary indexes using Neki. This setup enables efficient single-shard queries and transactions while maintaining full Postgres semantics.
DEPTH ■■■■■8 min read2 days ago
PostgreSQL
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03Shopify · architecture
We gave AI agents a mobile dev environment
Engineering teams outgrew their local machines while running parallel AI mobile development agents, constrained by heavy builds and lack of iOS tools on Linux. To solve this, they built Remote Dev using warm pools of remote Macs connected via SSH alongside separate Linux VMs for Android virtualization, paired with agent skills and live browser streaming. This setup shifted workflow bottlenecks, increasing AI-merged pull requests from 6 percent to 61 percent.
DEPTH ■■■■■9 min read3 days ago
Architecture Mobile
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04GitLab · tutorial
PHP2Shell: GitLab helps find and remediate critical PHP flaws
Critical remote code execution vulnerabilities known as PHP2Shell affect the PHP engine across multiple versions, requiring immediate runtime updates to fixed versions or patched distribution packages. The team utilized platform features like container scanning, code search, and automated agent workflows to locate PHP projects and remediate vulnerabilities. Operators must also check logs and environments for signs of compromise as framework updates and input validations cannot prevent these engine-level flaws.
DEPTH ■■■■■10 min read3 days ago
Architecture Security
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05Shopify · architecture
How Shopify turns open models into task experts
Shopify uses a continual learning flywheel to post-train open models, such as NVIDIA Nemotron, to serve as dedicated subagents for specific tasks like GraphQL generation. By optimizing inference and utilizing automated self-healing to generate training data from production failures, the team successfully created task experts that outperform frontier models while reducing serving costs by 90-98% and improving response times.
DEPTH ■■■■■5 min read4 days ago
Architecture
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06PlanetScale · practice
I don't trust ON DELETE CASCADE
Postgres ON DELETE CASCADE can cause production outages, replication failures, and performance degradation during large deletions or schema migrations due to long-running transactions holding locks and generating massive WAL. The author recommends using soft deletes with batched background cleanup instead to keep transactions short and bounded. This approach avoids table locks and simplifies data recovery.
DEPTH ■■■■■5 min read4 days ago
PostgreSQL
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07Shopify · architecture
Cross-store product clustering across millions of stores
Shopify needed to group identical product listings from millions of different merchants under a single global identifier. To solve this at scale without comparing every possible pair, the team implemented a multi-stage pipeline using vector retrieval, a tiered scoring system of ML models, and a bounded graph clustering approach. This architecture successfully standardizes product data across the entire platform while controlling computational costs.
DEPTH ■■■■■13 min read5 days ago
Architecture
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08Cloudflare · architecture
Under the hood, off the hot path: How D1 scales to millions of databases
Cloudflare scaled its serverless SQLite database, D1, to support millions of databases by decoupling the control plane from the data plane. The team used distributed metadata caching via Workers KV, optimized database connections through Hyperdrive, and implemented a self-correcting heartbeat mechanism. This architecture prevents centralized bottlenecks and allows D1 to handle rapid on-demand database creation efficiently.
DEPTH ■■■■■22 min read12 days ago
Databases
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09PlanetScale · architecture
What gets TOASTed in Postgres
This post examines how PostgreSQL uses TOAST to manage oversized rows and variable-length columns through compression and off-row storage. It compares the pglz and LZ4 compression algorithms across different workloads, highlighting trade-offs in search windows, file sizes, and CPU overhead. The analysis demonstrates how these mechanisms impact storage efficiency and read performance for large text and JSON data.
DEPTH ■■■■■12 min read13 days ago
PostgreSQL
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10PlanetScale · performance
TIN v1.0.6: 2-3x faster, with support for stemming
Following competitive benchmarks that highlighted opportunities for optimization, the developers of the TIN full-text search index for Postgres released versions 1.0.4 and 1.0.6. These updates added Snowball stemming for 18 languages, optimized top-k sorting queries with deterministic tiebreakers, and improved overall query performance. As a result, benchmark tests show that TIN outperforms competing extensions across conjunction, disjunction, and phrase query workloads.
DEPTH ■■■■■8 min read2 weeks ago
PostgreSQL
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11Grab · architecture
Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs
Grab addressed runtime data reliability issues in its data mesh by implementing automated Data Production Issues (DPIs). The system uses an incident orchestrator to detect contract breaches, a Data Health API for cross-platform root cause analysis, and Hugo for automated remediation. This approach resolved 86.9 percent of incidents automatically and made mean time to resolve six times faster.
DEPTH ■■■■■12 min read8 weeks ago
Databases Observability
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12Stack Overflow · practice
Taking a look under your agent’s hood
This post discusses the challenges and methods of applying observability to AI agents, which combine traditional distributed systems telemetry with new requirements for monitoring LLMs and multi-step tool calls. The discussion highlights how non-deterministic agent behavior and increased speed of execution make continuous production feedback loops essential for upstream development. Ultimately, integrating observability data into automated testing and development loops helps teams iterate on agent performance.
DEPTH ■■■■■25 min read2 weeks ago
Machine Learning Observability
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13PlanetScale · scaling
Handling hot shards
Growing applications often encounter hot shards when certain tenants or data subsets outgrow initial sharding strategies based on workspace IDs. To mitigate this, teams can scale up resources, isolate large tenants into specific key ranges, or fundamentally change the shard key for problematic tables based on access patterns. Neki facilitates these adjustments by allowing declarative data topologies and online resharding without downtime.
DEPTH ■■■■■7 min read3 weeks ago
MySQL PostgreSQL
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