# Query (disambiguation) The term "query" can mean multiple things: query-string as part of an URL sql-query to query a database This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## One or two nameservers? DevFeed: [One or two nameservers?]() Original publisher: [Read original article]() Author: Geoff Huston Published: 2026-09-15T06:01:49Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Internet](), [Query (disambiguation)](), [Server](), [Caching]() Tags: [2026](), [africa](), [article](), [china](), [dns](), [europe](), [experiment](), [india](), [internet](), [ipv4](), [ipv6](), [measurement](), [measurements](), [recursive-resolver](), [repeat](), [responses](), [server](), [tech-matters]() ### AI overview This article reports an experiment testing whether serving a DNS zone with two authoritative dual-stack nameservers changes repeated queries. Compared with one nameserver, two nameservers increased the single-query completion rate from 58% to 71% and reduced the average queries per test from 3.43 to 2.57. ### Source excerpt Do multiple dual-stack nameservers increase or decrease repeat DNS queries? The results of this experiment were a complete surprise. ## Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI DevFeed: [Beyond the Dashboard: Accelerating Real-Time Intelligence in the Age of AI]() Original publisher: [Read original article]() Author: Kostiantyn Okhrimenko Published: 2026-08-27T11:15:58Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [semantic-layer](), [genai](), [analytics stack](), [Large Language Model](), [Artificial Intelligence](), [data](), [dashboards](), [Query (disambiguation)]() Tags: [agent](), [ai](), [ai-tools](), [analytics-stack](), [bi-tools](), [dashboards](), [data](), [genai](), [real-time](), [self-service](), [self-serving-analytics](), [semantic-layer](), [sql]() ### AI overview The article examines how GenAI-driven natural-language interfaces can help stakeholders obtain trusted data answers without repeatedly interrupting data and engineering teams. It argues that a robust semantic layer is necessary to make self-service analytics reliable and precise. ### Source excerpt When an urgent request for a report or dashboard arrives, often just before an executive meeting, data and engineering teams must drop planned work to respond. One request may be reasonable, but repeated interruptions come at a cost: important work, such as scaling infrastructure, improving reliability, models optimization, gets pushed back, while quick, one-off dashboards become more technical debt to maintain. For managers and other decision-makers, the need is real: they require reliable data to make decisions quickly. But getting an answer often depends on someone who knows SQL, understands the data structure, and has time to help. When those people are already busy, the question waits, even when the answer is sitting in the data warehouse. By the time the report is ready, the decision window may have passed. This is not just a prioritization issue. We need a better way for people to get trusted answers quickly without constantly pulling teams away from building and improving the data platform. All of the above can be illustrated by the image: Image 1: Typical reporting circleWhat we will talk about The explosion of GenAI over the last few years has shifted the focus for the modern analytics stack. We are evolving beyond traditional Data Democratization, which often gave teams access to complex pre-AI tools without clear governance, toward natural language data interaction: asking questions in plain English -- Talk to your data concept. In the traditional stack, the "interface" to data was either a dashboard or a SQL editor. This created a high barrier to entry that caused the friction. By properly architecting and utilizing GenAI-driven tools, we can finally bridge the gap between intent and insight. Talk to your data is a self-serve ecosystem where any stakeholder can bypass the traditional ticketing queue and, instead of waiting for an engineer to interpret a requirement and translate it into a query, the user engages with a specialised agent. The challenge, h ## ADD COLUMN is not always free, and the lock queue is what takes you down DevFeed: [ADD COLUMN is not always free, and the lock queue is what takes you down]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [PostgreSQL](), [incident](), [Query (disambiguation)](), [migration](), [MySQL]() Tags: [ddl](), [incident](), [locking](), [migration](), [migrations](), [mysql](), [postgresql](), [query](), [queues](), [schema-design](), [timeout]() ### AI overview Adding a nullable column can cause an outage even when the schema change itself is nearly instantaneous. In PostgreSQL, ALTER TABLE may wait for a long-running query, and subsequent queries can queue behind the waiting lock request. Setting a short lock_timeout and retrying can prevent the migration from taking down the table. ### Source excerpt Modern PostgreSQL adds a column with a default as a metadata change, so the table is never rewritten and the migration itself is instant. The outage comes from lock acquisition instead: ALTER TABLE needs an ACCESS EXCLUSIVE lock, and while it waits behind one long-running query, every subsequent query queues behind the waiting ALTER because the lock queue is ordered. Setting lock_timeout to a couple of seconds and retrying converts that from an outage into a no-op. ## Covering Indexes and Index-Only Scans in PostgreSQL DevFeed: [Covering Indexes and Index-Only Scans in PostgreSQL]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-18T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [PostgreSQL](), [Query (disambiguation)](), [data]() Tags: [b-tree](), [cache](), [covering](), [covering-index](), [database-performance](), [explain](), [heap](), [indexes](), [indexing](), [innodb](), [pages](), [postgresql](), [query](), [storage](), [vacuum]() ### AI overview This tutorial explains why a normal index scan may still be slow: after finding matching entries, the database follows a pointer into the table for each row. Covering indexes store the selected columns in the index and can avoid those heap reads. In PostgreSQL, index-only scans also depend on the visibility map marking pages all-visible, while SELECT * prevents the technique from being fully effective. ### Source excerpt A normal index scan finds matching rows and then follows a pointer into the table for every one of them, which is a random read per row. A covering index stores the columns the query selects, so the engine answers entirely from the index and skips those reads. In PostgreSQL this only works when the visibility map marks the pages all-visible, so an unvacuumed table will report Heap Fetches in EXPLAIN and give back most of the gain. SELECT star defeats the technique completely. ## Why your index is not being used, and why the planner is usually right DevFeed: [Why your index is not being used, and why the planner is usually right]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-16T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Query (disambiguation)](), [MySQL](), [bug]() Tags: [bug](), [cardinality](), [database-performance](), [explain](), [function](), [indexes](), [indexing](), [mysql](), [postgresql](), [predicate](), [query](), [query-planner]() ### AI overview This tutorial explains why database indexes may not be used even when they exist. It focuses on inaccurate cardinality estimates caused by stale statistics, predicates that prevent index matching, implicit casts, and cases where sequential scans are the cheaper choice. ### Source excerpt An unused index is almost never a planner bug. It is usually a predicate the planner cannot match to the index, such as a function or an implicit cast applied to the column, or a cardinality estimate that is wrong because statistics are stale. When the estimate is right and the planner still refuses, it is often correct: past a few percent of the table, random access through an index costs more than reading the table sequentially. The diagnostic that matters is the gap between estimated and actual rows in EXPLAIN ANALYZE. ## Hybrid Search Patterns with Postgres and pgvector DevFeed: [Hybrid Search Patterns with Postgres and pgvector]() Original publisher: [Read original article]() Author: Christopher Winslett Published: 2026-07-30T15:00:00Z Content type: article Language: en Sources: [CrunchyData Blog]() Topics: [Embeddings](), [Query (disambiguation)](), [data]() Tags: [ann](), [caching](), [hybrid-search](), [performance](), [pgvector](), [postgres](), [search](), [vector](), [vector-search](), [vectors-llms]() ### AI overview This article explains why filtered vector search in Postgres creates a tradeoff between recall and performance. It discusses how ANN indexes such as HNSW and IVFFlat differ from B-tree indexes, and introduces approaches including pgvector iterative scans, partial indexes, oversampling, and caching. ### Source excerpt Filtered vector search in Postgres forces a tradeoff between recall and speed because ANN indexes and WHERE clauses do not intersect like B-trees. Read about pgvector iterative scans, partial HNSW indexes, oversampling with pg_stats, caching, and more. ## Learning a few things about running SQLite DevFeed: [Learning a few things about running SQLite]() Original publisher: [Read original article]() Author: Julia Evans Published: 2026-07-17T00:00:00Z Content type: article Language: en Sources: [Julia Evans]() Topics: [SQLite](), [Django](), [Object-relational mapping](), [Query (disambiguation)](), [Statistics]() Tags: [blog](), [django](), [orm](), [search](), [sqlite](), [statistics]() ### AI overview A personal account of operating SQLite in production for a small Django website. The article describes how running ANALYZE dramatically improved an SQLite FTS5 query and discusses the difficulty of cleaning up large numbers of rows without blocking other database writers. ### Source excerpt Hello! I've been working on a Django site recently, and I decided to use SQLite as the database. When I was getting started with using SQLite as database for a website I read a bunch of blog posts about how it is totally fine to use SQLite in production for a small site and I think it is totally fine, but what I did not fully appreciate is that SQLite is still a database, databases are complicated, and I do not know a lot about operating databases. So here are a couple of small things I've been learning about running SQLite. This is the 4th website I've used SQLite for, and I think this one is harder because with the power of the Django ORM I've been making the database do more work than I was previously without Django. I started by turning on WAL mode like all the blog posts said to do and hoping for the best. ANALYZE is apparently important Today I was running a query (using SQLite's FTS5 for full-text search) on a table with 4000 rows and it took 5 seconds. That seemed wrong to me: computers are fast! It turned out that what I needed to do was to run ANALYZE! Immediately the problem query went from taking 5 seconds to like 0.05 seconds (or some other number small enough that I didn't care to investigate further). I still don't know exactly what went wrong in the query plan, but my best guess is that it was some sort of accidentally quadratic thing. ANALYZE generates "statistics" (I guess about the number of rows in each table? and presumably other things?) so that the query planner can make better choices. Maybe one day I'll learn to read a query plan. cleaning up the database is tricky Occasionally I've run into situations where I accidentally put a bunch of rows in my database that I don't want to be there (for example completed tasks from django-tasks-db), and I want to clean them up. What's happened to me a few times in this case is: I run some kind of command to clean up the rows The command takes more than 5 seconds, since there are a lot of rows (though I ## The HTTP QUERY Method and RFC 10008 DevFeed: [The HTTP QUERY Method and RFC 10008]() Original publisher: [Read original article]() Author: ratneshmaurya2311@gmail.com (Ratnesh Maurya) Published: 2026-07-03T00:00:00Z Content type: tutorial Language: en Sources: [Ratn Labs]() Topics: [HTTP](), [Query (disambiguation)]() Tags: [api-design](), [backend](), [examples](), [http](), [query](), [web-development](), [web-development-http-backend-api-design]() ### AI overview This tutorial explains the HTTP QUERY method described in RFC 10008, including why GET and POST may be insufficient for complex searches and how QUERY works through examples. ### Source excerpt HTTP finally has a QUERY method. Learn what RFC 10008 changes, why GET and POST fell short for complex searches, and how QUERY works -- with simple examples. ## British Columbia, Time Zones, and Postgres DevFeed: [British Columbia, Time Zones, and Postgres]() Original publisher: [Read original article]() Author: Christopher Winslett Published: 2026-06-16T12:00:00Z Content type: tutorial Language: en Sources: [CrunchyData Blog]() Topics: [DateTime](), [Query (disambiguation)](), [Ubuntu]() Tags: [postgres](), [production-postgres](), [sql](), [time](), [timezone](), [ubuntu]() ### AI overview This article explains how British Columbia's permanent move to UTC-7 affects PostgreSQL handling of future appointments and time-zone conversions. It warns that changed time-zone rules or outdated tzdata packages can cause stored appointments to display at the wrong local time. ### Source excerpt This year, British Column has moved to year-round Pacific Time. How does that affect date data? ## Worth Reading: Data Center Power, DNS Privacy, Internet Fragmentation, AI Security, and Microsoft Secure Boot DevFeed: [Worth Reading: Data Center Power, DNS Privacy, Internet Fragmentation, AI Security, and Microsoft Secure Boot]() Original publisher: [Read original article]() Author: Russ Published: 2026-05-29T13:42:09Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [datacenter](), [Query (disambiguation)](), [Internet](), [Security](), [Microsoft]() Tags: [data-center](), [dns](), [internet](), [microsoft](), [secure-boot](), [security](), [worth-reading]() ### AI overview A reading roundup touching on data center power demand, DNS-based tracking and censorship, AI security benchmarks, internet fragmentation, and the upcoming expiration of Microsoft Secure Boot certificates. ### Source excerpt The rising power demand of the data center industry almost appears like an industry running within the integrated grid but outside the usual paradigm of the traditional electric utility sector. Indeed, it should be treated as such. Much has been said about the use of the DNS as a means both of tracking the online behaviour of individual users and as a means of online censorship and control. Almost every online transaction starts with a DNS query, and if one were able to assemble the complete set of DNS queries generated by an individual user it would be possible to assemble a relatively complete profile of their online activity. Let's say you wanted to make sure that your AI is secure. Can you just maximize the security and privacy benchmark and call it a day? Nope. The internet is fragmenting. Not in the future. Now. At three different layers simultaneously. In June, Microsoft Secure Boot certificates are set to expire for the first time ever. ## Redpanda SQL is GA: the query engine that skips the pipeline DevFeed: [Redpanda SQL is GA: the query engine that skips the pipeline]() Original publisher: [Read original article]() Author: Matt Schumpert Published: 2026-05-27T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Query (disambiguation)](), [Apache Iceberg](), [data-engineering](), [Streaming](), [Kafka]() Tags: [data-engineering](), [migration](), [redpanda-byoc](), [sql](), [streaming]() ### AI overview Redpanda SQL is a Postgres-based query engine available inside Redpanda BYOC clusters. It lets teams query live-streaming topics and historical Apache Iceberg tables in one SQL statement without an ETL pipeline or connector fleet, reducing ingestion delays and visibility gaps. ### Source excerpt Your warehouse can't query data that hasn't been ingested yet. Redpanda SQL can. Ad hoc SQL against live topics and Iceberg history, no ETL pipeline required. ## Using Dot Notation to Query Nested Fields in MongoDB DevFeed: [Using Dot Notation to Query Nested Fields in MongoDB]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-04-28T20:29:24Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Tutorial](), [Query (disambiguation)](), [Database]() Tags: [arrays](), [collection](), [database](), [filter](), [mongodb](), [object](), [tutorial]() ### AI overview This tutorial explains how to use MongoDB dot notation to query and filter nested fields in documents, including fields within nested objects and arrays. ### Source excerpt If you're just starting to dabble with MongoDB, you've probably come to a point where your documents are looking a little complex. These documents might have gone from flat, relational-looking pieces ... The post Using Dot Notation to Query Nested Fields in MongoDB appeared first on DataCamp. ## Waiting for PostgreSQL 19 - json format for COPY TO DevFeed: [Waiting for PostgreSQL 19 - json format for COPY TO]() Original publisher: [Read original article]() Author: depesz Published: 2026-03-29T12:34:22Z Content type: article Language: en Sources: [select \* from depesz;]() Topics: [PostgreSQL](), [JSON](), [export](), [data](), [Query (disambiguation)](), [interoperability]() Tags: [command](), [copy](), [data](), [developers](), [export](), [json](), [pg19](), [postgresql](), [query](), [stream](), [uncategorized](), [waiting]() ### AI overview The article discusses a PostgreSQL 19 patch adding a JSON format option to COPY TO. It allows table data or query results to be exported as a stream of JSON objects, one object per line, with an option to wrap the output in an array. ### Source excerpt On 20th of March 2026, Andrew Dunstan committed patch: json format for COPY TO This introduces the JSON format option for the COPY TO command, allowing users to export query results or table data directly as a stream of JSON objects (one per line, NDJSON style). The JSON format is currently supported only ... Continue reading "Waiting for PostgreSQL 19 - json format for COPY TO" ## Cursor Pagination vs Offset Pagination: Which One Should You Use? DevFeed: [Cursor Pagination vs Offset Pagination: Which One Should You Use?]() Original publisher: [Read original article]() Published: 2026-03-11T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [API](), [Databases](), [SQL](), [Query (disambiguation)]() Tags: [api](), [apis](), [b-tree](), [database](), [index](), [indexes](), [query-planner](), [rest-apis]() ### AI overview This tutorial compares offset, cursor, and keyset pagination for REST APIs. It explains that offset pagination becomes slower at deep pages because the database scans and discards preceding rows, while concurrent inserts can cause duplicates or skipped results. Cursor pagination uses an indexed position for more consistent performance but does not support random page access; keyset pagination generalizes the approach to arbitrary sort orders. ### Source excerpt . [Cursor vs Offset Pagination](cursor-pagination-vs-offset-pagination-cover... ## pg\_plan\_advice: Plan Stability and User Planner Control for PostgreSQL? DevFeed: [pg\_plan\_advice: Plan Stability and User Planner Control for PostgreSQL?]() Original publisher: [Read original article]() Author: Robert Haas (noreply@blogger.com) Published: 2026-03-04T17:55:00Z Content type: opinion Language: en Sources: [Robert Haas]() Topics: [PostgreSQL](), [JOIN](), [modules](), [Query (disambiguation)]() Tags: [join](), [modules](), [plan](), [planner](), [postgresql](), [query](), [test]() ### AI overview The article presents a proposed PostgreSQL 19 patch set introducing the pg_plan_advice, pg_collect_advice, and pg_stash_advice contrib modules. It demonstrates how pg_plan_advice can generate plan advice strings, preserve selected planner decisions, or vary advice to produce a different join plan. ### Source excerpt I'm proposing a very ambitious patch set for PostgreSQL 19. Only time will tell whether it ends up in the release, but I can't resist using this space to give you a short demonstration of what it can do. The patch set introduces three new contrib modules, currently called pg_plan_advice, pg_collect_advice, and pg_stash_advice. Read more " ## The Schema-Driven LLM Query Pattern DevFeed: [The Schema-Driven LLM Query Pattern]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-02-27T15:09:32Z Content type: tutorial Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [Large Language Model](), [Structured-data](), [Security](), [data](), [pandas](), [Query (disambiguation)]() Tags: [ai](), [blog](), [data](), [databases](), [graphql](), [llm](), [pandas](), [rest](), [schema](), [security](), [structured]() ### AI overview This tutorial presents the Schema-Driven LLM Query Pattern. Instead of sending raw data to an external LLM, it sends the data schema, receives query syntax, and executes the query locally to reduce data exposure and token usage. ### Source excerpt Typically, when working with an LLM, you send your data along with a prompt and receive a response. But this pattern often introduces issues, especially with public LLMs, due to security concerns and token bloat. There is a different pattern we can use: the Schema-Driven LLM Query Pattern. With ## Row Locks With Joins Can Produce Surprising Results in PostgreSQL DevFeed: [Row Locks With Joins Can Produce Surprising Results in PostgreSQL]() Original publisher: [Read original article]() Author: Haki Benita Published: 2026-02-23T22:00:00Z Content type: tutorial Language: en Sources: [Haki Benita]() Topics: [PostgreSQL](), [Concurrency](), [Databases](), [Query (disambiguation)]() Tags: [articles](), [concurrency](), [database](), [locks](), [postgresql](), [query](), [sql]() ### AI overview This article explains a PostgreSQL edge case in which row locks used with joins can produce surprising results, including a query returning no rows despite a valid, enforced foreign key. It uses a concurrent car-ownership update scenario and suggests ways to prevent the issue. ### Source excerpt You execute a query that joins two tables with a valid an enforces foreign key and it returns no results. How is it possible? We thought it wasn't possible, but a recent incident revealed an edge case we never thought about. In this article I show how under some circumstances row locks with joins can produce surprising results, and suggest ways to prevent it. ## Bringing Application-Level Observability to CockroachDB: Query Tagging DevFeed: [Bringing Application-Level Observability to CockroachDB: Query Tagging]() Original publisher: [Read original article]() Author: Kevin Ngo Published: 2026-02-06T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [observability](), [Microservice](), [Databases](), [OpenTelemetry](), [Query (disambiguation)](), [SQL]() Tags: [cockroachdb](), [database](), [feature-flags](), [latency](), [microservices](), [observability](), [opentelemetry](), [sql](), [tracing]() ### AI overview This article explains how CockroachDB query tagging uses SQL comments and the open-source SQLcommenter specification to add application context to database queries. The tags help correlate database performance issues and errors with specific microservices, routes, features, and other application metadata, improving observability and troubleshooting in distributed systems. ### Source excerpt Building mission-critical applications on CockroachDB demands robust observability. In complex, microservices-oriented architectures, it can be especially hard to correlate activity and pinpoint problems between different application services and the database layer. ## Engineering Den: implementing a new query manager (demo) DevFeed: [Engineering Den: implementing a new query manager (demo)]() Original publisher: [Read original article]() Author: Grzegorz Dudek Published: 2026-01-27T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Demo](), [Query (disambiguation)](), [SQL](), [Streaming](), [Testing](), [debug]() Tags: [debug](), [demo](), [engineering](), [sql](), [streaming]() ### AI overview This Engineering Den article presents a demo of Redpanda's refactored query manager. It explains how a deterministic state machine improves query lifecycle management, scheduling, cancellation, restart behavior, cluster stability, logging, and debuggability at scale. ### Source excerpt Clear state, predictable behavior, fewer surprises. That's what happened after we refactored our scheduler. Here's what we did and a demo comparing the old with the new. ## Unconventional PostgreSQL Optimizations DevFeed: [Unconventional PostgreSQL Optimizations]() Original publisher: [Read original article]() Author: Haki Benita Published: 2026-01-19T22:00:00Z Content type: tutorial Language: en Sources: [Haki Benita]() Topics: [PostgreSQL](), [Optimization](), [Database](), [Query (disambiguation)](), [Inheritance]() Tags: [articles](), [database-optimization](), [optimization](), [partitioning](), [performance](), [postgresql](), [query](), [vacuum]() ### AI overview This article presents unconventional PostgreSQL optimization techniques. It explains how constraint exclusion can use check constraints to avoid scanning a table for impossible conditions, while noting that enabling it broadly can add planning overhead; partition pruning is enabled by default for partitioned tables. ### Source excerpt When it comes to database optimization, developers often reach for the same old tools: rewrite the query slightly differently, slap an index on a column, denormalize, analyze, vacuum, cluster, repeat. Conventional techniques are effective, but sometimes being creative can really pay off! ## What's new in ClickStack. December '25. DevFeed: [What's new in ClickStack. December '25.]() Original publisher: [Read original article]() Author: The ClickStack Team Published: 2025-12-30T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [Open Source](), [dashboards](), [Query (disambiguation)]() Tags: [clickhouse](), [dashboards](), [observability](), [open-source](), [performance](), [speed]() ### AI overview ClickStack's December update introduces intelligent use of ClickHouse Materialized Views in the HyperDX UI. Registered views can automatically accelerate charts, searches, and dashboards by selecting an efficient view for each query, improving performance for large deployments. ### Source excerpt ClickStack's December update introduces the intelligent use of Materialized Views that automatically accelerate charts and dashboards, delivering faster insights at scale with zero user effort. ## New functions you might have missed in 2025 DevFeed: [New functions you might have missed in 2025]() Original publisher: [Read original article]() Author: Mark Needham Published: 2025-12-23T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Code](), [Query (disambiguation)](), [data](), [Authentication]() Tags: [2025](), [blog](), [blog-post](), [clickhouse](), [code](), [cryptographic](), [data](), [integrity]() ### AI overview This article highlights selected ClickHouse functions introduced in 2025, including functions for pattern matching in map values, calculating geometry perimeters, and generating HMAC signatures. It also shows queries for counting and listing newly introduced functions. ### Source excerpt Over 100 functions were added to ClickHouse in 2025. These were some of my favorites. ## Do you really need tsvector column? DevFeed: [Do you really need tsvector column?]() Original publisher: [Read original article]() Author: depesz Published: 2025-11-03T12:34:33Z Content type: article Language: en Sources: [select \* from depesz;]() Topics: [data](), [Query (disambiguation)](), [Algorithm]() Tags: [algorithm](), [benchmark](), [comparison](), [data](), [function](), [gin](), [gist](), [index](), [postgresql](), [query](), [tsearch](), [tsvector](), [uncategorized]() ### AI overview This article tests whether a materialized tsvector column is necessary for full-text search. Using a large Wikipedia-derived dataset, it compares a tsvector column with an expression-based function index and reports that the tested query took about 4.5 minutes with the materialized column versus almost an hour with the function-based index. ### Source excerpt When using tsearch one usually, often, creates a tsvector column to put data in, and then create index on it. But, do you really need the index? I wrote once already that you don't have to, but then a person talked with me on IRC, and pointed this section of docs: One advantage of the ... Continue reading "Do you really need tsvector column?" ## Book Review - Troubleshooting Java DevFeed: [Book Review - Troubleshooting Java]() Original publisher: [Read original article]() Author: vladmihalcea Published: 2025-10-15T08:28:01Z Content type: opinion Language: en Sources: [Vlad Mihalcea]() Topics: [Java](), [debugging](), [Optimization](), [ide](), [Logging](), [Query (disambiguation)](), [Databases]() Tags: [book](), [book-review](), [code-analysis](), [debugger](), [debugging](), [developer](), [ide](), [java](), [logging](), [optimization](), [performance](), [performance-tuning](), [review](), [sql](), [troubleshooting]() ### AI overview A review of the second edition of Troubleshooting Java by Laurentiu Spilca. The book covers code analysis, logging, debugging, profiling, SQL query investigation, lock monitoring, and deadlock troubleshooting for Java developers. ### Source excerpt Introduction My friend, Laurentiu Spilca, has been working on the second edition of his Troubleshooting Java book, and when I was asked to review it, I was more than happy to do it since the book features a lot of topics that I'm also very interested in, such as performance tuning and query optimization. Audience This book is useful for any Java developer because it teaches you how to get the best out of your IDE debugger, as well as showing you how to profile Java applications and fix very complex issues,... Read More The post Book Review - Troubleshooting Java appeared first on Vlad Mihalcea. [Next page]()