<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Diffbrief · Databases</title><description>Daily databases stories worth an engineer&apos;s time.</description><link>https://diffbrief.tatsatpandey.com/</link><language>en-us</language><item><title>One Hash Bloom Filters explains a compact probabilistic data structure</title><link>https://calabro.io/ohbf</link><guid isPermaLink="true">https://calabro.io/ohbf</guid><description>Bloom filters can avoid expensive database lookups; the excerpt says Bluesky uses them to save millions of Scylla queries per second. The production context makes the data-structure trade-offs relevant to engineers building high-throughput systems.</description><pubDate>Wed, 21 Oct 2026 06:38:00 GMT</pubDate><category>Databases</category><category>deep-dive</category></item><item><title>PlanetScale Neki adds read-only replicas and smaller Postgres nodes</title><link>https://planetscale.com/blog/neki-updates-and-preparing-for-production</link><guid isPermaLink="true">https://planetscale.com/blog/neki-updates-and-preparing-for-production</guid><description>The sharded Postgres platform added read-only replicas and Metal nodes as small as 1/8 vCPU, 1 GB RAM and 10 GB of NVMe storage. These changes give teams more options when evaluating a distributed Postgres deployment.</description><pubDate>Wed, 21 Oct 2026 06:38:00 GMT</pubDate><category>Databases</category><category>release</category></item><item><title>One-hash Bloom filters can spare Bluesky millions of database queries per second</title><link>https://www.calabro.io/ohbf</link><guid isPermaLink="true">https://www.calabro.io/ohbf</guid><description>Bloom filters can rule out keys without disk access, and Bluesky uses them to avoid millions of Scylla queries per second. The post explores a one-hash variant with potential lessons for latency-sensitive data systems.</description><pubDate>Tue, 20 Oct 2026 06:31:51 GMT</pubDate><category>Databases</category><category>deep-dive</category></item><item><title>BigQuery Python UDFs can send structured logs to Cloud Logging</title><link>https://cloud.google.com/blog/products/data-analytics/python-udf-logging-for-pipeline-observability-in-bigquery/</link><guid isPermaLink="true">https://cloud.google.com/blog/products/data-analytics/python-udf-logging-for-pipeline-observability-in-bigquery/</guid><description>Python UDFs can use the standard logging library to report row-level errors to Cloud Logging, allowing bad records to be isolated without halting a query. The end-to-end example shows a practical observability pattern for resilient data pipelines.</description><pubDate>Tue, 20 Oct 2026 06:31:51 GMT</pubDate><category>Databases</category><category>guide</category></item><item><title>KiviDB shards a Redis-compatible in-memory keyspace across threads</title><link>https://github.com/kividbio/kividb</link><guid isPermaLink="true">https://github.com/kividbio/kividb</guid><description>KiviDB supports RESP2 and RESP3, shards its keyspace across threads, and uses io_uring on Linux with a Tokio fallback elsewhere. Those implementation choices are relevant to engineers evaluating in-memory data stores, though the excerpt gives no benchmark results.</description><pubDate>Tue, 20 Oct 2026 06:31:51 GMT</pubDate><category>Databases</category><category>tool</category></item><item><title>A5 1.0 freezes its global grid layout and 64-bit cell IDs</title><link>https://a5geo.org/blog/2026/10/14/a5-v1</link><guid isPermaLink="true">https://a5geo.org/blog/2026/10/14/a5-v1</guid><description>A5 1.0 commits to stable cell IDs and grid layout, alongside feature and performance improvements; one example aggregates 631 million fire detections. Stable geospatial identifiers matter to teams building durable analytics pipelines.</description><pubDate>Tue, 20 Oct 2026 06:31:51 GMT</pubDate><category>Databases</category><category>release</category></item><item><title>Hanami’s database layer separates relations, repositories, and structs</title><link>https://aaronmallen.me/writing/hanami-why-model-citizens</link><guid isPermaLink="true">https://aaronmallen.me/writing/hanami-why-model-citizens</guid><description>The overview explains how Hanami organizes its database layer around relations, repositories, and structs rather than a traditional Rails-style model layer. It offers a useful framework-design comparison for engineers evaluating data-access patterns.</description><pubDate>Tue, 20 Oct 2026 06:31:51 GMT</pubDate><category>Databases</category><category>guide</category></item><item><title>CLP searches compressed logs while scaling to petabytes</title><link>https://www.usenix.org/system/files/osdi21-rodrigues.pdf</link><guid isPermaLink="true">https://www.usenix.org/system/files/osdi21-rodrigues.pdf</guid><description>CLP combines lossless, domain-specific compression with search on compressed text, reporting over 13× faster ingestion than Elasticsearch and Splunk and petabyte-scale operation. The approach offers a substantial storage and observability trade-off for large log archives.</description><pubDate>Sun, 18 Oct 2026 06:29:34 GMT</pubDate><category>Databases</category><category>paper</category></item><item><title>BigQuery queries cross-cloud data in place through the Iceberg REST catalog</title><link>https://cloud.google.com/blog/products/data-analytics/cross-cloud-conversational-analytics-with-borderless-lakehouse/</link><guid isPermaLink="true">https://cloud.google.com/blog/products/data-analytics/cross-cloud-conversational-analytics-with-borderless-lakehouse/</guid><description>The described design uses the Apache Iceberg REST catalog to query remote datasets without copying them into a central staging warehouse, avoiding synchronization lag, storage costs and egress fees. It is a useful pattern for cross-cloud analytics and AI workloads.</description><pubDate>Sun, 18 Oct 2026 06:29:34 GMT</pubDate><category>Databases</category><category>deep-dive</category></item><item><title>PlanetScale adds Postgres and Neki databases on Azure</title><link>https://planetscale.com/blog/planetscale-is-now-on-azure</link><guid isPermaLink="true">https://planetscale.com/blog/planetscale-is-now-on-azure</guid><description>PlanetScale is available in three Azure regions with high availability across three availability zones, backups and Query Insights; Vitess is not yet supported. Teams can now colocate these database workloads with Azure-based applications.</description><pubDate>Sun, 18 Oct 2026 06:29:34 GMT</pubDate><category>Databases</category><category>release</category></item><item><title>Postgres 19 slips to November after developers drop up to 10 features</title><link>https://www.theregister.com/databases/2026/10/16/postgres-19-delayed-after-ai-reviewers-find-more-bugs-than-humans-can-fix/5303471</link><guid isPermaLink="true">https://www.theregister.com/databases/2026/10/16/postgres-19-delayed-after-ai-reviewers-find-more-bugs-than-humans-can-fix/5303471</guid><description>Postgres 19 is now expected around November 19, with eight to ten significant features deferred after AI review tooling surfaced more bugs than developers could fix. The delay is a useful example of how review capacity and tooling can affect a major database release.</description><pubDate>Sat, 17 Oct 2026 06:01:58 GMT</pubDate><category>Databases</category><category>news</category></item><item><title>DuckDB converts JSON text columns into typed columns directly in SQL</title><link>https://duckdb.org/2026/10/16/json-to-typed-columns.html</link><guid isPermaLink="true">https://duckdb.org/2026/10/16/json-to-typed-columns.html</guid><description>DuckDB can infer and adjust a schema from JSON stored in a VARCHAR column, then produce typed columns without exporting the data to a file. This removes an unnecessary round trip from common analytics and ingestion workflows.</description><pubDate>Sat, 17 Oct 2026 06:01:58 GMT</pubDate><category>Databases</category><category>guide</category></item><item><title>LLM-assisted review is driving PostgreSQL 19 feature reverts</title><link>https://lwn.net/Articles/1100182/</link><guid isPermaLink="true">https://lwn.net/Articles/1100182/</guid><description>PostgreSQL’s beta has seen features reverted over architectural flaws and a long tail of edge-case defects, with LLM-assisted review surfacing issues faster. The resulting workload shows how stronger automated review can raise quality bars while increasing release-management costs.</description><pubDate>Fri, 16 Oct 2026 06:22:18 GMT</pubDate><category>Databases</category><category>essay</category></item><item><title>Postgres Weekly covers agent-memory results and a queue slowed by savepoints</title><link>https://postgresweekly.com/issues/669</link><guid isPermaLink="true">https://postgresweekly.com/issues/669</guid><description>The roundup reports that raw conversation retrieval combined with extracted facts beat either alone in a LongMemEval rerun, while per-job savepoints slowed a Postgres queue as workers increased. Both findings offer practical leads for engineers building agent memory or database-backed workloads.</description><pubDate>Fri, 16 Oct 2026 06:22:18 GMT</pubDate><category>Databases</category><category>news</category></item><item><title>Iceberg tables are trees of JSON, Avro and Parquet files</title><link>https://duckdb.org/2026/10/13/inside-iceberg-table.html</link><guid isPermaLink="true">https://duckdb.org/2026/10/13/inside-iceberg-table.html</guid><description>The guide walks through Iceberg metadata, manifest lists, manifests, data files and delete files, showing how DuckDB can inspect each layer with SQL. Understanding that structure helps diagnose slow or incorrect table queries without treating the table as a black box.</description><pubDate>Fri, 16 Oct 2026 06:22:18 GMT</pubDate><category>Databases</category><category>deep-dive</category></item><item><title>Stripe Data Pipeline adds real-time sync to Postgres</title><link>https://stripe.com/blog/real-time-stripe-data-postgres</link><guid isPermaLink="true">https://stripe.com/blog/real-time-stripe-data-postgres</guid><description>Stripe Data Pipeline can now keep Postgres databases current with Stripe data, replacing custom sync integrations. The change may simplify the data plumbing behind applications that depend on current payment and account state.</description><pubDate>Fri, 16 Oct 2026 06:22:18 GMT</pubDate><category>Databases</category><category>release</category></item><item><title>Pg_vectorsearch adds quantized hierarchical routing for PostgreSQL ANN search</title><link>https://github.com/timescale/pg_vectorsearch</link><guid isPermaLink="true">https://github.com/timescale/pg_vectorsearch</guid><description>The alpha PostgreSQL extension combines hierarchical routing, RaBitQ quantization and SIMD distance calculations for approximate nearest-neighbor indexes, with dynamic updates. It could be worth evaluating for vector-search workloads, but the excerpt provides no comparative benchmark results.</description><pubDate>Thu, 15 Oct 2026 06:22:59 GMT</pubDate><category>Databases</category><category>tool</category></item><item><title>InnoDB row storage favors point reads while indexes add write cost</title><link>https://marcpaul.io/logs/same-job-two-engines-innodb-and-iceberg-internals/</link><guid isPermaLink="true">https://marcpaul.io/logs/same-job-two-engines-innodb-and-iceberg-internals/</guid><description>The comparison explains that InnoDB stores rows together, making selective reads efficient, while each B-tree index adds work to writes; it also introduces undo logs for concurrent reads and writes. These trade-offs help engineers reason about OLTP and OLAP workload choices.</description><pubDate>Thu, 15 Oct 2026 06:22:59 GMT</pubDate><category>Databases</category><category>deep-dive</category></item><item><title>PostgreSQL 19 beta saw 8–10 significant features reverted</title><link>http://peter.eisentraut.org/blog/2026/10/09/the-reverts-will-continue-until-morale-improves</link><guid isPermaLink="true">http://peter.eisentraut.org/blog/2026/10/09/the-reverts-will-continue-until-morale-improves</guid><description>A PostgreSQL developer reflects on 8–10 significant features reverted after the 19 beta began, including some late in the beta period. The discussion offers lessons for database contributors and teams managing risky releases.</description><pubDate>Wed, 14 Oct 2026 06:38:54 GMT</pubDate><category>Databases</category><category>essay</category></item><item><title>Postgres TOAST compression can trigger when a value grows by one byte</title><link>https://planetscale.com/blog/what-gets-toasted-in-postgres</link><guid isPermaLink="true">https://planetscale.com/blog/what-gets-toasted-in-postgres</guid><description>The piece shows that a 2,000-byte value remains uncompressed while a 2,001-byte value is compressed to 26 bytes, then explains row-page limits and TOAST behavior. It also covers Postgres 19&apos;s shift to LZ4, useful context for storage and compression trade-offs.</description><pubDate>Tue, 13 Oct 2026 08:14:15 GMT</pubDate><category>Databases</category><category>deep-dive</category></item><item><title>The data cube generalizes SQL aggregation across multiple dimensions</title><link>https://arxiv.org/pdf/cs/0701155</link><guid isPermaLink="true">https://arxiv.org/pdf/cs/0701155</guid><description>The paper defines the cube operator as a relational generalization of grouping, cross-tabs, roll-ups, and subtotals, and discusses efficient computation. Its model remains useful context for engineers working on analytical query systems.</description><pubDate>Tue, 13 Oct 2026 08:14:15 GMT</pubDate><category>Databases</category><category>paper</category></item><item><title>APSW exposes SQLite&apos;s full C API to Python</title><link>https://github.com/rogerbinns/apsw</link><guid isPermaLink="true">https://github.com/rogerbinns/apsw</guid><description>The wrapper provides SQLite features including virtual tables, VFS, JSON and session support, with synchronous and async usage. It is a useful option when Python applications need more than the standard sqlite3 interface.</description><pubDate>Mon, 12 Oct 2026 07:53:06 GMT</pubDate><category>Databases</category><category>tool</category></item><item><title>ClickHouse C adds a header-only client for the Native protocol</title><link>https://github.com/ClickHouse/clickhouse-c</link><guid isPermaLink="true">https://github.com/ClickHouse/clickhouse-c</guid><description>The C client decodes ClickHouse Native Format blocks and can read `clickhouse-local` output from a pipe without TCP or compression. Its PostgreSQL-extension-oriented design may interest engineers integrating ClickHouse with other systems.</description><pubDate>Mon, 12 Oct 2026 07:53:06 GMT</pubDate><category>Databases</category><category>tool</category></item><item><title>JSON tiles accelerate analytics on flexible JSON data</title><link>https://db.cit.tum.de/~durner/papers/json-tiles-sigmod21.pdf</link><guid isPermaLink="true">https://db.cit.tum.de/~durner/papers/json-tiles-sigmod21.pdf</guid><description>JSON tiles detect important keys, extract them for relational queries, and collect statistics for the optimizer while supporting changing schemas. The paper reports scan performance similar to columnar storage, a useful approach for semi-structured data systems.</description><pubDate>Sun, 11 Oct 2026 07:44:51 GMT</pubDate><category>Databases</category><category>paper</category></item><item><title>Project Timestamper cryptographically timestamps Common Crawl’s 339 billion snapshots</title><link>https://projecttimestamper.org/blog/common-crawl/</link><guid isPermaLink="true">https://projecttimestamper.org/blog/common-crawl/</guid><description>Project Timestamper says it has cryptographically timestamped Common Crawl’s 339 billion web snapshots, spanning 18 years and more than 10 petabytes. Provenance for large web archives matters to engineers working with data at scale.</description><pubDate>Sun, 11 Oct 2026 07:44:51 GMT</pubDate><category>Databases</category><category>news</category></item><item><title>DuckLake stores Parquet data with SQL-managed catalog metadata</title><link>https://github.com/duckdb/ducklake</link><guid isPermaLink="true">https://github.com/duckdb/ducklake</guid><description>The DuckDB extension reads and writes an open lakehouse format that keeps metadata in a catalog database and data in Parquet files. Its SQL-based approach is relevant to engineers evaluating lakehouse storage and query workflows.</description><pubDate>Fri, 09 Oct 2026 07:53:56 GMT</pubDate><category>Databases</category><category>tool</category></item><item><title>TIN v1.0.6 adds stemming and claims a 2–3× speedup</title><link>https://planetscale.com/blog/tin-v106</link><guid isPermaLink="true">https://planetscale.com/blog/tin-v106</guid><description>The update adds stemming and heap-attribute tiebreakers for top-k ordering; PlanetScale says TIN v1.0.6 is 2–3× faster and has regained the lead in its comparisons. Database engineers evaluating Postgres full-text search can assess the index’s feature and performance claims.</description><pubDate>Fri, 09 Oct 2026 07:53:56 GMT</pubDate><category>Databases</category><category>release</category></item><item><title>PostgreSQL separates static query rewrites from data-driven plan optimization</title><link>https://theconsensus.dev/p/2026/09/13/query-plan-rewriting-in-postgresql.html</link><guid isPermaLink="true">https://theconsensus.dev/p/2026/09/13/query-plan-rewriting-in-postgresql.html</guid><description>The piece distinguishes statically knowable rewrites, such as folding `i+0`, from optimization decisions that depend on statistics and data. Understanding that boundary helps engineers reason about query plans and database performance.</description><pubDate>Fri, 09 Oct 2026 07:53:56 GMT</pubDate><category>Databases</category><category>deep-dive</category></item><item><title>A small DuckDB file can catalog Parquet data without storing it</title><link>https://duckdb.org/2026/10/07/view-only-mode.html</link><guid isPermaLink="true">https://duckdb.org/2026/10/07/view-only-mode.html</guid><description>DuckDB can hold only view definitions pointing to Parquet files on object storage, creating a shareable catalog that stays a few hundred kilobytes regardless of dataset size. This pattern separates query access from data copies and can simplify sharing data lake datasets.</description><pubDate>Thu, 08 Oct 2026 07:59:03 GMT</pubDate><category>Databases</category><category>guide</category></item></channel></rss>