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Deep, recent reads ranked by depth and freshness.

  1. 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

    DatabasesPerformanceRedis

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  2. 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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  3. From 2,500 requests to 24: Rebuilding star history without the stargazers

    GitHub faced abuse and performance issues because tools were scraping user identities to reconstruct repository star history. The engineering team collaborated with maintainers to build a purpose-built aggregate endpoint that returns daily star counts without identities and filters by time before aggregation. This reduced request volume from thousands to a few dozen and eliminated heavy database scans.

    DEPTH ■■■■■8 min read11 days ago

    Microservices

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  4. 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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  5. Inside Hyperdrive: How we solved a rare Hyperdrive MySQL caching bug

    During the beta of MySQL support for Hyperdrive, engineers investigated rare empty query responses and connection hangs occurring in 0.001% of requests. They discovered that sharing the data center cache between client and endpoint nodes caused protocol capability mismatches when translating MySQL wire messages. The team resolved the issue by standardizing the cache to always store responses encoded with canonical Hyperdrive capabilities.

    DEPTH ■■■■■9 min read11 days ago

    MySQL

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  6. 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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  7. 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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  8. Smarter personalization: How property data helps us understand user price sensitivity

    Existing user personalization systems fail to systematically account for residential property values, leading to one-dimensional profiles and regional blind spots. A conceptual four-step method is proposed that combines public property data, large language models, building-level price estimation, and hierarchical clustering to classify user affluence. The approach remains unimplemented, with future viability depending on technical validation, data quality, and privacy approvals.

    DEPTH ■■■■■4 min read2 weeks ago

    Data EngineeringMachine Learning

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  9. GrabMart data for faster disease outbreak detection

    The organization proposes a conceptual data pipeline to analyze aggregated GrabMart transaction data for symptom-related product demand, aiming to supplement clinical surveillance for disease outbreaks. The design relies on data aggregation, statistical baseline comparisons, and privacy safeguards like differential privacy without involving individual health inference. The system remains in an early planning phase requiring rigorous validation, privacy review, and public health oversight.

    DEPTH ■■■■■5 min read12 days ago

    Data EngineeringMachine Learning

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  10. 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

    MySQLPostgreSQL

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